Sign language dataset is essential in sign language recognition and translation(SLRT). Current public sign language datasets are small and lack diversity, which does not meet the practical application requirements for...Sign language dataset is essential in sign language recognition and translation(SLRT). Current public sign language datasets are small and lack diversity, which does not meet the practical application requirements for SLRT. However, making a large-scale and diverse sign language dataset is difficult as sign language data on the Internet is scarce. In making a large-scale and diverse sign language dataset, some sign language data qualities are not up to standard. This paper proposes a two information streams transformer(TIST) model to judge whether the quality of sign language data is qualified. To verify that TIST effectively improves sign language recognition(SLR), we make two datasets, the screened dataset and the unscreened dataset. In this experiment, this paper uses visual alignment constraint(VAC) as the baseline model. The experimental results show that the screened dataset can achieve better word error rate(WER) than the unscreened dataset.展开更多
Traffic sign detection is a critical component of driving systems.Single-stage network-based traffic sign detection algorithms,renowned for their fast detection speeds and high accuracy,have become the dominant approa...Traffic sign detection is a critical component of driving systems.Single-stage network-based traffic sign detection algorithms,renowned for their fast detection speeds and high accuracy,have become the dominant approach in current practices.However,in complex and dynamic traffic scenes,particularly with smaller traffic sign objects,challenges such as missed and false detections can lead to reduced overall detection accuracy.To address this issue,this paper proposes a detection algorithm that integrates edge and shape information.Recognizing that traffic signs have specific shapes and distinct edge contours,this paper introduces an edge feature extraction branch within the backbone network,enabling adaptive fusion with features of the same hierarchical level.Additionally,a shape prior convolution module is designed to replaces the first two convolutional modules of the backbone network,aimed at enhancing the model's perception ability for specific shape objects and reducing its sensitivity to background noise.The algorithm was evaluated on the CCTSDB and TT100k datasets,and compared to YOLOv8s,the mAP50 values increased by 3.0%and 10.4%,respectively,demonstrating the effectiveness of the proposed method in improving the accuracy of traffic sign detection.展开更多
Traffic sign detection is an important part of autonomous driving,and its recognition accuracy and speed are directly related to road traffic safety.Although convolutional neural networks(CNNs)have made certain breakt...Traffic sign detection is an important part of autonomous driving,and its recognition accuracy and speed are directly related to road traffic safety.Although convolutional neural networks(CNNs)have made certain breakthroughs in this field,in the face of complex scenes,such as image blur and target occlusion,the traffic sign detection continues to exhibit limited accuracy,accompanied by false positives and missed detections.To address the above problems,a traffic sign detection algorithm,You Only Look Once-based Skip Dynamic Way(YOLO-SDW)based on You Only Look Once version 8 small(YOLOv8s),is proposed.Firstly,a Skip Connection Reconstruction(SCR)module is introduced to efficiently integrate fine-grained feature information and enhance the detection accuracy of the algorithm in complex scenes.Secondly,a C2f module based on Dynamic Snake Convolution(C2f-DySnake)is proposed to dynamically adjust the receptive field information,improve the algorithm’s feature extraction ability for blurred or occluded targets,and reduce the occurrence of false detections and missed detections.Finally,the Wise Powerful IoU v2(WPIoUv2)loss function is proposed to further improve the detection accuracy of the algorithm.Experimental results show that the average precision mAP@0.5 of YOLO-SDW on the TT100K dataset is 89.2%,and mAP@0.5:0.95 is 68.5%,which is 4%and 3.3%higher than the YOLOv8s baseline,respectively.YOLO-SDW ensures real-time performance while having higher accuracy.展开更多
Sign language is a primary mode of communication for individuals with hearing impairments,conveying meaning through hand shapes and hand movements.Contrary to spoken or written languages,sign language relies on the re...Sign language is a primary mode of communication for individuals with hearing impairments,conveying meaning through hand shapes and hand movements.Contrary to spoken or written languages,sign language relies on the recognition and interpretation of hand gestures captured in video data.However,sign language datasets remain relatively limited compared to those of other languages,which hinders the training and performance of deep learning models.Additionally,the distinct word order of sign language,unlike that of spoken language,requires context-aware and natural sentence generation.To address these challenges,this study applies data augmentation techniques to build a Korean Sign Language dataset and train recognition models.Recognized words are then reconstructed into complete sentences.The sign recognition process uses OpenCV and MediaPipe to extract hand landmarks from sign language videos and analyzes hand position,orientation,and motion.The extracted features are converted into time-series data and fed into a Long Short-Term Memory(LSTM)model.The proposed recognition framework achieved an accuracy of up to 81.25%,while the sentence generation achieved an accuracy of up to 95%.The proposed approach is expected to be applicable not only to Korean Sign Language but also to other low-resource sign languages for recognition and translation tasks.展开更多
Reliable detection of traffic signs and lights(TSLs)at long range and under varying illumination is essen-tial for improving the perception and safety of autonomous driving systems(ADS).Traditional object detection mo...Reliable detection of traffic signs and lights(TSLs)at long range and under varying illumination is essen-tial for improving the perception and safety of autonomous driving systems(ADS).Traditional object detection models often exhibit significant performance degradation in real-world environments characterized by high dynamic range and complex lighting conditions.To overcome these limitations,this research presents FED-YOLOv10s,an improved and lightweight object detection framework based on You Only look Once v10(YOLOv10).The proposed model integrates a C2f-Faster block derived from FasterNet to reduce parameters and floating-point operations,an Efficient Multiscale Attention(EMA)mechanism to improve TSL-invariant feature extraction,and a deformable Convolution Networks v4(DCNv4)module to enhance multiscale spatial adaptability.Experimental findings demonstrate that the proposed architecture achieves an optimal balance between computational efficiency and detection accuracy,attaining an F1-score of 91.8%,and mAP@0.5 of 95.1%,while reducing parameters to 8.13 million.Comparative analyses across multiple traffic sign detection benchmarks demonstrate that FED-YOLOv10s outperforms state-of-the-art models in precision,recall,and mAP.These results highlight FED-YOLOv10s as a robust,efficient,and deployable solution for intelligent traffic perception in ADS.展开更多
Accurate and real-time traffic-sign detection is a cornerstone of Advanced Driver-Assistance Systems(ADAS)and autonomous vehicles.However,existing one-stage detectors miss distant signs,and two-stage pipelines are imp...Accurate and real-time traffic-sign detection is a cornerstone of Advanced Driver-Assistance Systems(ADAS)and autonomous vehicles.However,existing one-stage detectors miss distant signs,and two-stage pipelines are impractical for embedded deployment.To address this issue,we present YOLO-SMM,a lightweight two-stage framework.This framework is designed to augment the YOLOv8 baseline with three targeted modules.(1)SlimNeck replaces PAN/FPN with a CSP-OSA/GSConv fusion block,reducing parameters and FLOPs without compromising multi-scale detail.(2)The MCA model introduces row-and column-aware weights to selectively amplify small sign regions in cluttered scenes.(3)MPDIoU augments CIoU loss with a corner-distance term,supplying stable gradients for sub-20-pixel boxes and tightening localization.An evaluation of YOLO-SMMon the German Traffic Sign Recognition Benchmark(GTSRB)revealed that it attained 96.3% mAP50 and 93.1% mAP50-90 at a rate of 90.6 frames per second(FPS).This represents an improvement of+1.0% over previous performance benchmarks.Them APat 64×64 resolution was found to be 50% of the maximum attainable value,with an FPS of+8.3 when compared to YOLOv8.This result indicates superior performance in terms of accuracy and speed compared to YOLOv7,YOLOv5,RetinaNet,EfficientDet,and Faster R-CNN,all of which were operated under equivalent conditions.展开更多
Convolutional neural networks(CNNs)exhibit superior performance in image feature extraction,making them extensively used in the area of traffic sign recognition.However,the design of existing traffic sign recognition ...Convolutional neural networks(CNNs)exhibit superior performance in image feature extraction,making them extensively used in the area of traffic sign recognition.However,the design of existing traffic sign recognition algorithms often relies on expert knowledge to enhance the image feature extraction networks,necessitating image preprocessing and model parameter tuning.This increases the complexity of the model design process.This study introduces an evolutionary neural architecture search(ENAS)algorithm for the automatic design of neural network models tailored for traffic sign recognition.By integrating the construction parameters of residual network(ResNet)into evolutionary algorithms(EAs),we automatically generate lightweight networks for traffic sign recognition,utilizing blocks as the fundamental building units.Experimental evaluations on the German traffic sign recognition benchmark(GTSRB)dataset reveal that the algorithm attains a recognition accuracy of 99.32%,with a mere 2.8×10^(6)parameters.Experimental results comparing the proposed method with other traffic sign recognition algorithms demonstrate that the method can more efficiently discover neural network architectures,significantly reducing the number of network parameters while maintaining recognition accuracy.展开更多
To explore the influence of emergency evacuation signs on passenger behavior during subway fires and improve evacuation efficiency in emergencies,this paper proposes a dynamic emergency evacuation sign system.A simula...To explore the influence of emergency evacuation signs on passenger behavior during subway fires and improve evacuation efficiency in emergencies,this paper proposes a dynamic emergency evacuation sign system.A simulation platform integrating building information modeling(BIM)and virtual reality(VR)technologies was em-ployed to create subway fire evacuation scenarios using both the current and proposed dynamic emergency evacuation signage systems.Through simulation experiments,fine-grained microscopic data on passenger behavior was collected.Seven indicators were selected to assess evacuation efficiency and wayfinding difficulty.The analysis explored the influence of evacuation signs on passenger behavior in both overall and decision-making areas,thereby validating the effectiveness of the new emergency evacuation signage system.The results show that the dynamic evacuation signage system significantly improves overall passenger evacuation efficiency and reduces decision-making errors.It also improves wayfinding efficiency in critical decision areas by reducing the need for direction identification,minimizing stopping times,and lowering the frequency of decision errors.The method for evaluating the effects of emergency evacuation signs on passenger evacuation behavior proposed in this study provides a robust theoretical basis for the design and optimization of emergency-oriented signs.展开更多
Accessible communication based on sign language recognition(SLR)is the key to emergency medical assistance for the hearing-impaired community.Balancing the capture of both local and global information in SLR for emerg...Accessible communication based on sign language recognition(SLR)is the key to emergency medical assistance for the hearing-impaired community.Balancing the capture of both local and global information in SLR for emergency medicine poses a significant challenge.To address this,we propose a novel approach based on the inter-learning of visual features between global and local information.Specifically,our method enhances the perception capabilities of the visual feature extractor by strategically leveraging the strengths of convolutional neural network(CNN),which are adept at capturing local features,and visual transformers which perform well at perceiving global features.Furthermore,to mitigate the issue of overfitting caused by the limited availability of sign language data for emergency medical applications,we introduce an enhanced short temporal module for data augmentation through additional subsequences.Experimental results on three publicly available sign language datasets demonstrate the efficacy of the proposed approach.展开更多
A graph whose edges are labeled either as positive or negative is called a signed graph.Hameed et al.introduced signed distance and distance compatibility in 2021,initially to characterize balanced signed graphs which...A graph whose edges are labeled either as positive or negative is called a signed graph.Hameed et al.introduced signed distance and distance compatibility in 2021,initially to characterize balanced signed graphs which have nice spectral properties.This article mainly studies the conjecture proposed by Shijin et al.on the distance compatibility of the direct product of signed graphs,and provides necessary and sufficient conditions for the distance compatibility of the direct product of signed graphs.Some further questions regarding distance compatibility are also posed.展开更多
This paper proposes a lightweight traffic sign detection system based on you only look once(YOLO).Firstly,the classification to fusion(C2f)structure is integrated into the backbone network,employing deformable convolu...This paper proposes a lightweight traffic sign detection system based on you only look once(YOLO).Firstly,the classification to fusion(C2f)structure is integrated into the backbone network,employing deformable convolution and bi-directional feature pyramid network(BiFPN)_Concat to improve the adaptability of the network.Secondly,the simple attention module(SimAm)is embedded to prioritize key features and reduce the complexity of the model after the C2f layer at the end of the backbone network.Next,the focal efficient intersection over union(EloU)is introduced to adjust the weights of challenging samples.Finally,we accomplish the design and deployment for the mobile app.The results demonstrate improvements,with the F1 score of 0.8987,mean average precision(mAP)@0.5 of 98.8%,mAP@0.5:0.95 of 75.6%,and the detection speed of 50 frames per second(FPS).展开更多
The representative collective digital signature,which was suggested by us,is built based on combining the advantages of group digital signature and collective digital signature.This collective digital signature schema...The representative collective digital signature,which was suggested by us,is built based on combining the advantages of group digital signature and collective digital signature.This collective digital signature schema helps to create a unique digital signature that deputizes a collective of people representing different groups of signers and may also include personal signers.The advantage of the proposed collective signature is that it can be built based on most of the well-known difficult problems such as the factor analysis,the discrete logarithm and finding modulo roots of large prime numbers and the current digital signature standards of the United States and Russian Federation.In this paper,we use the discrete logarithmic problem on prime finite fields,which has been implemented in the GOST R34.10-1994 digital signature standard,to build the proposed collective signature protocols.These protocols help to create collective signatures:Guaranteed internal integrity and fixed size,independent of the number of members involved in forming the signature.The signature built in this study,consisting of 3 components(U,R,S),stores the information of all relevant signers in the U components,thus tracking the signer and against the“disclaim of liability”of the signer later is possible.The idea of hiding the signer’s public key is also applied in the proposed protocols.This makes it easy for the signing group representative to specify which members are authorized to participate in the signature creation process.展开更多
The representative collective digital signature scheme allows the creation of a unique collective signature on document M that represents an entire signing community consisting of many individual signers and many diff...The representative collective digital signature scheme allows the creation of a unique collective signature on document M that represents an entire signing community consisting of many individual signers and many different signing groups,each signing group is represented by a group leader.On document M,a collective signature can be created using the representative digital signature scheme that represents an entire community consisting of individual signers and signing groups,each of which is represented by a group leader.The characteristic of this type of letter is that it consists of three elements(U,E,S),one of which(U)is used to store the information of all the signers who participated in the formation of the collective signature on document M.While storing this information is necessary to identify the signer and resolve disputes later,it greatly increases the size of signatures.This is considered a limitation of the collective signature representing 3 elements.In this paper,we propose and build a new type of collective signature,a collective signature representing 2 elements(E,S).In this case,the signature has been reduced in size,but it contains all the information needed to identify the signer and resolve disputes if necessary.To construct the approved group signature scheme,which is the basic scheme for the proposed representative collective signature schemes,we use the discrete logarithm problem on the prime finite field.At the end of this paper,we present the security analysis of the AGDS scheme and a performance evaluation of the proposed collective signature schemes.展开更多
There have been many digital signature schemes were developed based on the discrete logarithm problem on a finite field.In this study,we use the elliptic curve discrete logarithm problem to build new collective signat...There have been many digital signature schemes were developed based on the discrete logarithm problem on a finite field.In this study,we use the elliptic curve discrete logarithm problem to build new collective signature schemes.The cryptosystem on elliptic curve allows to generate digital signatures with the same level of security as other cryptosystems but with smaller keys.To extend practical applicability and enhance the security level of the group signature protocols,we propose two new types of collective digital signature schemes based on the discrete logarithm problem on the elliptic curve:i)the collective digital signature scheme shared by several signing groups and ii)the collective digital signature scheme shared by several signing groups and several individual signers.These two new types of collective signatures have combined the advantages of group digital signatures and collective digital signatures.These signatures have a fixed size and do not depend on the number of members participating in the creation of the final collective signature.One of the advantages of the proposed collective signature protocols is that they can be deployed on top of the available public key infrastructures.展开更多
The unique structure of signed networks,characterized by positive and negative edges,poses significant challenges for analyzing network topology.In recent years,various statistical algorithms have been developed to ad...The unique structure of signed networks,characterized by positive and negative edges,poses significant challenges for analyzing network topology.In recent years,various statistical algorithms have been developed to address this issue.However,there remains a lack of a unified framework to uncover the nontrivial properties inherent in signed network structures.To support developers,researchers,and practitioners in this field,we introduce a Python library named SNSAlib(Signed Network Structure Analysis),specifically designed to meet these analytical requirements.This library encompasses empirical signed network datasets,signed null model algorithms,signed statistics algorithms,and evaluation indicators.The primary objective of SNSAlib is to facilitate the systematic analysis of micro-and meso-structure features within signed networks,including node popularity,clustering,assortativity,embeddedness,and community structure by employing more accurate signed null models.Ultimately,it provides a robust paradigm for structure analysis of signed networks that enhances our understanding and application of signed networks.展开更多
Once a train stops in a tunnel section and requires emergency evacuation,the large distance between stations and long walking distances in the underground spaces of suburban railway systems pose potential risks to the...Once a train stops in a tunnel section and requires emergency evacuation,the large distance between stations and long walking distances in the underground spaces of suburban railway systems pose potential risks to the evacuation process on tunnel platforms,especially in complex environments.This study utilized Virtual Reality(VR)technology to construct a virtual experimental platform for tunnel evacuation in suburban railway systems,simulating different combinations of smoke and obstacle conditions.By requiring participants to wear VR glasses and walk on an omnidirectional treadmill for moving,as well as complete psychological questionnaires,the study reveals the influences of No Guiding(NG)signs,Wall-Guided(WG)signs,and Central axis Guidance(CG)signs on the movement abilities and psychological behaviors of participants contrastively.The results show that either smoke conditions or obstacle positions affect the mental stress of participants,and the guidance sign has a positive effect on reducing the mental stress.There is an inverse relationship between mental stress and movement abilities.WG and CG signs respectively lead participants to walk closer to walls and along the central axis,which is conducive to reducing the variation in participants’behavior characteristics when circumventing obstacles on the wall side or track side under smoke conditions,respectively.Additionally,CG signs reduce the speed fluctuations of participants before circumventing obstacles,improving the stability of the distance from the wall and speed under smoke conditions,compared to NG and WG signs.These findings contribute to understanding the evacuation psychological-behavioral-movement characteristics of pedestrians on evacuation platforms in suburban railway tunnels and provide a basis for improving the safety design of evacuation guidance signs.展开更多
Dynamic sign language recognition holds significant importance, particularly with the application of deep learning to address its complexity. However, existing methods face several challenges. Firstly, recognizing dyn...Dynamic sign language recognition holds significant importance, particularly with the application of deep learning to address its complexity. However, existing methods face several challenges. Firstly, recognizing dynamic sign language requires identifying keyframes that best represent the signs, and missing these keyframes reduces accuracy. Secondly, some methods do not focus enough on hand regions, which are small within the overall frame, leading to information loss. To address these challenges, we propose a novel Video Transformer Attention-based Network (VTAN) for dynamic sign language recognition. Our approach prioritizes informative frames and hand regions effectively. To tackle the first issue, we designed a keyframe extraction module enhanced by a convolutional autoencoder, which focuses on selecting information-rich frames and eliminating redundant ones from the video sequences. For the second issue, we developed a soft attention-based transformer module that emphasizes extracting features from hand regions, ensuring that the network pays more attention to hand information within sequences. This dual-focus approach improves effective dynamic sign language recognition by addressing the key challenges of identifying critical frames and emphasizing hand regions. Experimental results on two public benchmark datasets demonstrate the effectiveness of our network, outperforming most of the typical methods in sign language recognition tasks.展开更多
基金supported by the National Language Commission to research on sign language data specifications for artificial intelligence applications and test standards for language service translation systems (No.ZDI145-70)。
文摘Sign language dataset is essential in sign language recognition and translation(SLRT). Current public sign language datasets are small and lack diversity, which does not meet the practical application requirements for SLRT. However, making a large-scale and diverse sign language dataset is difficult as sign language data on the Internet is scarce. In making a large-scale and diverse sign language dataset, some sign language data qualities are not up to standard. This paper proposes a two information streams transformer(TIST) model to judge whether the quality of sign language data is qualified. To verify that TIST effectively improves sign language recognition(SLR), we make two datasets, the screened dataset and the unscreened dataset. In this experiment, this paper uses visual alignment constraint(VAC) as the baseline model. The experimental results show that the screened dataset can achieve better word error rate(WER) than the unscreened dataset.
基金supported by the National Natural Science Foundation of China(Grant Nos.62572057,62272049,U24A20331)Beijing Natural Science Foundation(Grant Nos.4232026,4242020)Academic Research Projects of Beijing Union University(Grant No.ZK10202404).
文摘Traffic sign detection is a critical component of driving systems.Single-stage network-based traffic sign detection algorithms,renowned for their fast detection speeds and high accuracy,have become the dominant approach in current practices.However,in complex and dynamic traffic scenes,particularly with smaller traffic sign objects,challenges such as missed and false detections can lead to reduced overall detection accuracy.To address this issue,this paper proposes a detection algorithm that integrates edge and shape information.Recognizing that traffic signs have specific shapes and distinct edge contours,this paper introduces an edge feature extraction branch within the backbone network,enabling adaptive fusion with features of the same hierarchical level.Additionally,a shape prior convolution module is designed to replaces the first two convolutional modules of the backbone network,aimed at enhancing the model's perception ability for specific shape objects and reducing its sensitivity to background noise.The algorithm was evaluated on the CCTSDB and TT100k datasets,and compared to YOLOv8s,the mAP50 values increased by 3.0%and 10.4%,respectively,demonstrating the effectiveness of the proposed method in improving the accuracy of traffic sign detection.
基金funded by Key research and development Program of Henan Province(No.251111211200)National Natural Science Foundation of China(Grant No.U2004163).
文摘Traffic sign detection is an important part of autonomous driving,and its recognition accuracy and speed are directly related to road traffic safety.Although convolutional neural networks(CNNs)have made certain breakthroughs in this field,in the face of complex scenes,such as image blur and target occlusion,the traffic sign detection continues to exhibit limited accuracy,accompanied by false positives and missed detections.To address the above problems,a traffic sign detection algorithm,You Only Look Once-based Skip Dynamic Way(YOLO-SDW)based on You Only Look Once version 8 small(YOLOv8s),is proposed.Firstly,a Skip Connection Reconstruction(SCR)module is introduced to efficiently integrate fine-grained feature information and enhance the detection accuracy of the algorithm in complex scenes.Secondly,a C2f module based on Dynamic Snake Convolution(C2f-DySnake)is proposed to dynamically adjust the receptive field information,improve the algorithm’s feature extraction ability for blurred or occluded targets,and reduce the occurrence of false detections and missed detections.Finally,the Wise Powerful IoU v2(WPIoUv2)loss function is proposed to further improve the detection accuracy of the algorithm.Experimental results show that the average precision mAP@0.5 of YOLO-SDW on the TT100K dataset is 89.2%,and mAP@0.5:0.95 is 68.5%,which is 4%and 3.3%higher than the YOLOv8s baseline,respectively.YOLO-SDW ensures real-time performance while having higher accuracy.
基金supported by the Institute of Information&Communications Technoljogy Planning&Evaluation(IITP)-Innovative Human Resource Development for Local Intellectualization Program grant funded by the Korea government(MSIT)(IITP-2026-RS-2022-00156334,50%)the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.2021R1C1C2011105,50%).
文摘Sign language is a primary mode of communication for individuals with hearing impairments,conveying meaning through hand shapes and hand movements.Contrary to spoken or written languages,sign language relies on the recognition and interpretation of hand gestures captured in video data.However,sign language datasets remain relatively limited compared to those of other languages,which hinders the training and performance of deep learning models.Additionally,the distinct word order of sign language,unlike that of spoken language,requires context-aware and natural sentence generation.To address these challenges,this study applies data augmentation techniques to build a Korean Sign Language dataset and train recognition models.Recognized words are then reconstructed into complete sentences.The sign recognition process uses OpenCV and MediaPipe to extract hand landmarks from sign language videos and analyzes hand position,orientation,and motion.The extracted features are converted into time-series data and fed into a Long Short-Term Memory(LSTM)model.The proposed recognition framework achieved an accuracy of up to 81.25%,while the sentence generation achieved an accuracy of up to 95%.The proposed approach is expected to be applicable not only to Korean Sign Language but also to other low-resource sign languages for recognition and translation tasks.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,Saudi Arabia under Grant No.IPP:172-830-2025.
文摘Reliable detection of traffic signs and lights(TSLs)at long range and under varying illumination is essen-tial for improving the perception and safety of autonomous driving systems(ADS).Traditional object detection models often exhibit significant performance degradation in real-world environments characterized by high dynamic range and complex lighting conditions.To overcome these limitations,this research presents FED-YOLOv10s,an improved and lightweight object detection framework based on You Only look Once v10(YOLOv10).The proposed model integrates a C2f-Faster block derived from FasterNet to reduce parameters and floating-point operations,an Efficient Multiscale Attention(EMA)mechanism to improve TSL-invariant feature extraction,and a deformable Convolution Networks v4(DCNv4)module to enhance multiscale spatial adaptability.Experimental findings demonstrate that the proposed architecture achieves an optimal balance between computational efficiency and detection accuracy,attaining an F1-score of 91.8%,and mAP@0.5 of 95.1%,while reducing parameters to 8.13 million.Comparative analyses across multiple traffic sign detection benchmarks demonstrate that FED-YOLOv10s outperforms state-of-the-art models in precision,recall,and mAP.These results highlight FED-YOLOv10s as a robust,efficient,and deployable solution for intelligent traffic perception in ADS.
基金supported by University of Malaya and Ministry of High Education-Malaysia via Fundamental Research Grant Scheme No.FRGS/1/2023/TK10/UM/02/3.
文摘Accurate and real-time traffic-sign detection is a cornerstone of Advanced Driver-Assistance Systems(ADAS)and autonomous vehicles.However,existing one-stage detectors miss distant signs,and two-stage pipelines are impractical for embedded deployment.To address this issue,we present YOLO-SMM,a lightweight two-stage framework.This framework is designed to augment the YOLOv8 baseline with three targeted modules.(1)SlimNeck replaces PAN/FPN with a CSP-OSA/GSConv fusion block,reducing parameters and FLOPs without compromising multi-scale detail.(2)The MCA model introduces row-and column-aware weights to selectively amplify small sign regions in cluttered scenes.(3)MPDIoU augments CIoU loss with a corner-distance term,supplying stable gradients for sub-20-pixel boxes and tightening localization.An evaluation of YOLO-SMMon the German Traffic Sign Recognition Benchmark(GTSRB)revealed that it attained 96.3% mAP50 and 93.1% mAP50-90 at a rate of 90.6 frames per second(FPS).This represents an improvement of+1.0% over previous performance benchmarks.Them APat 64×64 resolution was found to be 50% of the maximum attainable value,with an FPS of+8.3 when compared to YOLOv8.This result indicates superior performance in terms of accuracy and speed compared to YOLOv7,YOLOv5,RetinaNet,EfficientDet,and Faster R-CNN,all of which were operated under equivalent conditions.
基金supported by the National Natural Science Foundation of China(No.62066041).
文摘Convolutional neural networks(CNNs)exhibit superior performance in image feature extraction,making them extensively used in the area of traffic sign recognition.However,the design of existing traffic sign recognition algorithms often relies on expert knowledge to enhance the image feature extraction networks,necessitating image preprocessing and model parameter tuning.This increases the complexity of the model design process.This study introduces an evolutionary neural architecture search(ENAS)algorithm for the automatic design of neural network models tailored for traffic sign recognition.By integrating the construction parameters of residual network(ResNet)into evolutionary algorithms(EAs),we automatically generate lightweight networks for traffic sign recognition,utilizing blocks as the fundamental building units.Experimental evaluations on the German traffic sign recognition benchmark(GTSRB)dataset reveal that the algorithm attains a recognition accuracy of 99.32%,with a mere 2.8×10^(6)parameters.Experimental results comparing the proposed method with other traffic sign recognition algorithms demonstrate that the method can more efficiently discover neural network architectures,significantly reducing the number of network parameters while maintaining recognition accuracy.
基金Beijing Natural Science Foundation-Fengtai Rail Transit Frontier Research Joint Foundation(No.L211024),the National Natural Science Foundation of China(No.52072012).
文摘To explore the influence of emergency evacuation signs on passenger behavior during subway fires and improve evacuation efficiency in emergencies,this paper proposes a dynamic emergency evacuation sign system.A simulation platform integrating building information modeling(BIM)and virtual reality(VR)technologies was em-ployed to create subway fire evacuation scenarios using both the current and proposed dynamic emergency evacuation signage systems.Through simulation experiments,fine-grained microscopic data on passenger behavior was collected.Seven indicators were selected to assess evacuation efficiency and wayfinding difficulty.The analysis explored the influence of evacuation signs on passenger behavior in both overall and decision-making areas,thereby validating the effectiveness of the new emergency evacuation signage system.The results show that the dynamic evacuation signage system significantly improves overall passenger evacuation efficiency and reduces decision-making errors.It also improves wayfinding efficiency in critical decision areas by reducing the need for direction identification,minimizing stopping times,and lowering the frequency of decision errors.The method for evaluating the effects of emergency evacuation signs on passenger evacuation behavior proposed in this study provides a robust theoretical basis for the design and optimization of emergency-oriented signs.
基金supported by the National Natural Science Foundation of China(No.62376197)the Tianjin Science and Technology Program(No.23JCYBJC00360)the Tianjin Health Research Project(No.TJWJ2025MS045).
文摘Accessible communication based on sign language recognition(SLR)is the key to emergency medical assistance for the hearing-impaired community.Balancing the capture of both local and global information in SLR for emergency medicine poses a significant challenge.To address this,we propose a novel approach based on the inter-learning of visual features between global and local information.Specifically,our method enhances the perception capabilities of the visual feature extractor by strategically leveraging the strengths of convolutional neural network(CNN),which are adept at capturing local features,and visual transformers which perform well at perceiving global features.Furthermore,to mitigate the issue of overfitting caused by the limited availability of sign language data for emergency medical applications,we introduce an enhanced short temporal module for data augmentation through additional subsequences.Experimental results on three publicly available sign language datasets demonstrate the efficacy of the proposed approach.
基金Supported by the National Natural Science Foundation of China(Grant No.12071260)。
文摘A graph whose edges are labeled either as positive or negative is called a signed graph.Hameed et al.introduced signed distance and distance compatibility in 2021,initially to characterize balanced signed graphs which have nice spectral properties.This article mainly studies the conjecture proposed by Shijin et al.on the distance compatibility of the direct product of signed graphs,and provides necessary and sufficient conditions for the distance compatibility of the direct product of signed graphs.Some further questions regarding distance compatibility are also posed.
基金supported by the Shanxi Agricultural University Science and Technology Innovation Enhancement Project。
文摘This paper proposes a lightweight traffic sign detection system based on you only look once(YOLO).Firstly,the classification to fusion(C2f)structure is integrated into the backbone network,employing deformable convolution and bi-directional feature pyramid network(BiFPN)_Concat to improve the adaptability of the network.Secondly,the simple attention module(SimAm)is embedded to prioritize key features and reduce the complexity of the model after the C2f layer at the end of the backbone network.Next,the focal efficient intersection over union(EloU)is introduced to adjust the weights of challenging samples.Finally,we accomplish the design and deployment for the mobile app.The results demonstrate improvements,with the F1 score of 0.8987,mean average precision(mAP)@0.5 of 98.8%,mAP@0.5:0.95 of 75.6%,and the detection speed of 50 frames per second(FPS).
基金supported by Duy Tan University,Da Nang,Vietnam.
文摘The representative collective digital signature,which was suggested by us,is built based on combining the advantages of group digital signature and collective digital signature.This collective digital signature schema helps to create a unique digital signature that deputizes a collective of people representing different groups of signers and may also include personal signers.The advantage of the proposed collective signature is that it can be built based on most of the well-known difficult problems such as the factor analysis,the discrete logarithm and finding modulo roots of large prime numbers and the current digital signature standards of the United States and Russian Federation.In this paper,we use the discrete logarithmic problem on prime finite fields,which has been implemented in the GOST R34.10-1994 digital signature standard,to build the proposed collective signature protocols.These protocols help to create collective signatures:Guaranteed internal integrity and fixed size,independent of the number of members involved in forming the signature.The signature built in this study,consisting of 3 components(U,R,S),stores the information of all relevant signers in the U components,thus tracking the signer and against the“disclaim of liability”of the signer later is possible.The idea of hiding the signer’s public key is also applied in the proposed protocols.This makes it easy for the signing group representative to specify which members are authorized to participate in the signature creation process.
文摘The representative collective digital signature scheme allows the creation of a unique collective signature on document M that represents an entire signing community consisting of many individual signers and many different signing groups,each signing group is represented by a group leader.On document M,a collective signature can be created using the representative digital signature scheme that represents an entire community consisting of individual signers and signing groups,each of which is represented by a group leader.The characteristic of this type of letter is that it consists of three elements(U,E,S),one of which(U)is used to store the information of all the signers who participated in the formation of the collective signature on document M.While storing this information is necessary to identify the signer and resolve disputes later,it greatly increases the size of signatures.This is considered a limitation of the collective signature representing 3 elements.In this paper,we propose and build a new type of collective signature,a collective signature representing 2 elements(E,S).In this case,the signature has been reduced in size,but it contains all the information needed to identify the signer and resolve disputes if necessary.To construct the approved group signature scheme,which is the basic scheme for the proposed representative collective signature schemes,we use the discrete logarithm problem on the prime finite field.At the end of this paper,we present the security analysis of the AGDS scheme and a performance evaluation of the proposed collective signature schemes.
文摘There have been many digital signature schemes were developed based on the discrete logarithm problem on a finite field.In this study,we use the elliptic curve discrete logarithm problem to build new collective signature schemes.The cryptosystem on elliptic curve allows to generate digital signatures with the same level of security as other cryptosystems but with smaller keys.To extend practical applicability and enhance the security level of the group signature protocols,we propose two new types of collective digital signature schemes based on the discrete logarithm problem on the elliptic curve:i)the collective digital signature scheme shared by several signing groups and ii)the collective digital signature scheme shared by several signing groups and several individual signers.These two new types of collective signatures have combined the advantages of group digital signatures and collective digital signatures.These signatures have a fixed size and do not depend on the number of members participating in the creation of the final collective signature.One of the advantages of the proposed collective signature protocols is that they can be deployed on top of the available public key infrastructures.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.72371031,62173065,62476045)Fundamental Research Funds for the Central Universities(Grant No.124330008)。
文摘The unique structure of signed networks,characterized by positive and negative edges,poses significant challenges for analyzing network topology.In recent years,various statistical algorithms have been developed to address this issue.However,there remains a lack of a unified framework to uncover the nontrivial properties inherent in signed network structures.To support developers,researchers,and practitioners in this field,we introduce a Python library named SNSAlib(Signed Network Structure Analysis),specifically designed to meet these analytical requirements.This library encompasses empirical signed network datasets,signed null model algorithms,signed statistics algorithms,and evaluation indicators.The primary objective of SNSAlib is to facilitate the systematic analysis of micro-and meso-structure features within signed networks,including node popularity,clustering,assortativity,embeddedness,and community structure by employing more accurate signed null models.Ultimately,it provides a robust paradigm for structure analysis of signed networks that enhances our understanding and application of signed networks.
基金supported by the National Natural Science Foundation of China(Grant Number 52472322)the Shanghai Sailing Program(Grant Number 21YF1415800)+1 种基金the Open Project of Key Laboratory of Advanced Public Transportation Science(Grant Number 2023-APTS-05)the Shanghai SASAC Enterprise Innovation and Capability Enhancement Project(Grant Number 2022016,2023020).
文摘Once a train stops in a tunnel section and requires emergency evacuation,the large distance between stations and long walking distances in the underground spaces of suburban railway systems pose potential risks to the evacuation process on tunnel platforms,especially in complex environments.This study utilized Virtual Reality(VR)technology to construct a virtual experimental platform for tunnel evacuation in suburban railway systems,simulating different combinations of smoke and obstacle conditions.By requiring participants to wear VR glasses and walk on an omnidirectional treadmill for moving,as well as complete psychological questionnaires,the study reveals the influences of No Guiding(NG)signs,Wall-Guided(WG)signs,and Central axis Guidance(CG)signs on the movement abilities and psychological behaviors of participants contrastively.The results show that either smoke conditions or obstacle positions affect the mental stress of participants,and the guidance sign has a positive effect on reducing the mental stress.There is an inverse relationship between mental stress and movement abilities.WG and CG signs respectively lead participants to walk closer to walls and along the central axis,which is conducive to reducing the variation in participants’behavior characteristics when circumventing obstacles on the wall side or track side under smoke conditions,respectively.Additionally,CG signs reduce the speed fluctuations of participants before circumventing obstacles,improving the stability of the distance from the wall and speed under smoke conditions,compared to NG and WG signs.These findings contribute to understanding the evacuation psychological-behavioral-movement characteristics of pedestrians on evacuation platforms in suburban railway tunnels and provide a basis for improving the safety design of evacuation guidance signs.
基金supported by the National Natural Science Foundation of China under Grant Nos.62076117 and 62166026the Jiangxi Provincial Key Laboratory of Virtual Reality under Grant No.2024SSY03151.
文摘Dynamic sign language recognition holds significant importance, particularly with the application of deep learning to address its complexity. However, existing methods face several challenges. Firstly, recognizing dynamic sign language requires identifying keyframes that best represent the signs, and missing these keyframes reduces accuracy. Secondly, some methods do not focus enough on hand regions, which are small within the overall frame, leading to information loss. To address these challenges, we propose a novel Video Transformer Attention-based Network (VTAN) for dynamic sign language recognition. Our approach prioritizes informative frames and hand regions effectively. To tackle the first issue, we designed a keyframe extraction module enhanced by a convolutional autoencoder, which focuses on selecting information-rich frames and eliminating redundant ones from the video sequences. For the second issue, we developed a soft attention-based transformer module that emphasizes extracting features from hand regions, ensuring that the network pays more attention to hand information within sequences. This dual-focus approach improves effective dynamic sign language recognition by addressing the key challenges of identifying critical frames and emphasizing hand regions. Experimental results on two public benchmark datasets demonstrate the effectiveness of our network, outperforming most of the typical methods in sign language recognition tasks.
