Accurate detection of smoke and fire sources is critical for early fire warning and environmental monitoring.However,conventional detection approaches are highly susceptible to noise,illumination variations,and comple...Accurate detection of smoke and fire sources is critical for early fire warning and environmental monitoring.However,conventional detection approaches are highly susceptible to noise,illumination variations,and complex environmental conditions,which often reduce detection accuracy and real-time performance.To address these limitations,we propose Lightweight and Precise YOLO(LP-YOLO),a high-precision detection framework that integrates a self-attention mechanism with a feature pyramid,built upon YOLOv8.First,to overcome the restricted receptive field and parameter redundancy of conventional Convolutional Neural Networks(CNNs),we design an enhanced backbone based on Wavelet Convolutions(WTConv),which expands the receptive field through multifrequency convolutional processing.Second,a Bidirectional Feature Pyramid Network(BiFPN)is employed to achieve bidirectional feature fusion,enhancing the representation of smoke features across scales.Third,to mitigate the challenge of ambiguous object boundaries,we introduce the Frequency-aware Feature Fusion(FreqFusion)module,in which the Adaptive Low-Pass Filter(ALPF)reduces intra-class inconsistencies,the offset generator refines boundary localization,and the Adaptive High-Pass Filter(AHPF)recovers high-frequency details lost during down-sampling.Experimental evaluations demonstrate that LP-YOLO significantly outperforms the baseline YOLOv8,achieving an improvement of 9.3%in mAP@50 and 9.2%in F1-score.Moreover,the model is 56.6%and 32.4%smaller than YOLOv7-tiny and EfficientDet,respectively,while maintaining real-time inference speed at 238 frames per second(FPS).Validation on multiple benchmark datasets,including D-Fire,FIRESENSE,and BoWFire,further confirms its robustness and generalization ability,with detection accuracy consistently exceeding 82%.These results highlight the potential of LP-YOLO as a practical solution with high accuracy,robustness,and real-time performance for smoke and fire source detection.展开更多
Two key challenges raised by a product images classification system are classification precision and classification time. In some categories, classification precision of the latest techniques, in the product images cl...Two key challenges raised by a product images classification system are classification precision and classification time. In some categories, classification precision of the latest techniques, in the product images classification system, is still low. In this paper, we propose a local texture descriptor termed fan refined local binary pattern, which captures more detailed information by integrating the spatial distribution into the local binary pattern feature. We compare our approach with different methods on a subset of product images on Amazon/e Bay and parts of PI100 and experimental results have demonstrated that our proposed approach is superior to the current existing methods. The highest classification precision is increased by 21% and the average classification time is reduced by 2/3.展开更多
Accurate pancreas segmentation is critical for the diagnosis and management of diseases of the pancreas. It is challenging to precisely delineate pancreas due to the highly variations in volume, shape and location. In...Accurate pancreas segmentation is critical for the diagnosis and management of diseases of the pancreas. It is challenging to precisely delineate pancreas due to the highly variations in volume, shape and location. In recent years, coarse-to-fine methods have been widely used to alleviate class imbalance issue and improve pancreas segmentation accuracy. However,cascaded methods could be computationally intensive and the refined results are significantly dependent on the performance of its coarse segmentation results. To balance the segmentation accuracy and computational efficiency, we propose a Discriminative Feature Attention Network for pancreas segmentation, to effectively highlight pancreas features and improve segmentation accuracy without explicit pancreas location. The final segmentation is obtained by applying a simple yet effective post-processing step. Two experiments on both public NIH pancreas CT dataset and abdominal BTCV multi-organ dataset are individually conducted to show the effectiveness of our method for 2 D pancreas segmentation. We obtained average Dice Similarity Coefficient(DSC) of 82.82±6.09%, average Jaccard Index(JI) of 71.13± 8.30% and average Symmetric Average Surface Distance(ASD) of 1.69 ± 0.83 mm on the NIH dataset. Compared to the existing deep learning-based pancreas segmentation methods, our experimental results achieve the best average DSC and JI value.展开更多
This paper presents a novel approach for camera pose refinement based on neural radiance fields(NeRF)by introducing semantic feature consistency to enhance robustness.NeRF has been successfully applied to camera pose ...This paper presents a novel approach for camera pose refinement based on neural radiance fields(NeRF)by introducing semantic feature consistency to enhance robustness.NeRF has been successfully applied to camera pose estimation by inverting the rendering process given an observed RGB image and an initial pose estimate.However,previous methods only adopted photometric consistency for pose optimization,which is prone to be trapped in local minima.To address this problem,we introduce semantic feature consistency into the existing framework.Specifically,we utilize high-level features extracted from a convolutional neural network(CNN)pre-trained for image recognition,and maintain consistency of such features between observed and rendered images during the optimization procedure.Unlike the color values at each pixel,these features contain rich semantic information shared within local regions and can be more robust to appearance changes from different viewpoints.Since it is computationally expensive to render a full image with NeRF for feature extraction from CNN,we propose an efficient way to estimate the features of individually rendered pixels by projecting them to a nearby reference image and interpolating its feature maps.Extensive experiments show that our method greatly outperforms the baseline method on both synthetic objects and real-world large indoor scenes,increasing the accuracy of pose estimation by over 6.4%.展开更多
In the research of software reuse, feature models have been widely adopted to capture, organize and reuse the requirements of a set of similar applications in a software do- main. However, the construction, especially...In the research of software reuse, feature models have been widely adopted to capture, organize and reuse the requirements of a set of similar applications in a software do- main. However, the construction, especially the refinement, of feature models is a labor-intensive process, and there lacks an effective way to aid domain engineers in refining feature models. In this paper, we propose a new approach to support interactive refinement of feature models based on the view updating technique. The basic idea of our approach is to first extract features and relationships of interest from a possibly large and complicated feature model, then organize them into a comprehensible view, and finally refine the feature model through modifications on the view. The main characteristics of this approach are twofold： a set of powerful rules （as the slicing criterion） to slice the feature model into a view auto- matically, and a novel use of a bidirectional transformation language to make the view updatable. We have successfully developed a tool, and a nontrivial case study shows the feasi- bility of this approach.展开更多
Onboard visual object tracking in unmanned aerial vehicles(UAVs)has attractedmuch interest due to its versatility.Meanwhile,due to high precision,Siamese networks are becoming hot spots in visual object tracking.Howev...Onboard visual object tracking in unmanned aerial vehicles(UAVs)has attractedmuch interest due to its versatility.Meanwhile,due to high precision,Siamese networks are becoming hot spots in visual object tracking.However,most Siamese trackers fail to balance the tracking accuracy and time within onboard limited computational resources of UAVs.To meet the tracking precision and real-time requirements,this paper proposes a Siamese dense pixel-level network for UAV object tracking named SiamDPL.Specifically,the Siamese network extracts features of the search region and the template region through a parameter-shared backbone network,then performs correlationmatching to obtain the candidate regionwith high similarity.To improve the matching effect of template and search features,this paper designs a dense pixel-level feature fusion module to enhance the matching ability by pixel-wise correlation and enrich the feature diversity by dense connection.An attention module composed of self-attention and channel attention is introduced to learn global context information and selectively emphasize the target feature region in the spatial and channel dimensions.In addition,a target localization module is designed to improve target location accuracy.Compared with other advanced trackers,experiments on two public benchmarks,which are UAV123@10fps and UAV20L fromthe unmanned air vehicle123(UAV123)dataset,show that SiamDPL can achieve superior performance and low complexity with a running speed of 100.1 fps on NVIDIA TITAN RTX.展开更多
In modern energy systems,substations are the core of electricity transmission and distribution.However,similar appearance and small size pose significant challenges for automatic identification of electrical devices.T...In modern energy systems,substations are the core of electricity transmission and distribution.However,similar appearance and small size pose significant challenges for automatic identification of electrical devices.To address these issues,we collect and annotate the substation rotated device dataset(SRDD).Further,feature fusion and feature refinement network(F3RNet)are constructed based on the classic structure pattern of backbone-neck-head.Considering the similar appearance of electrical devices,the deconvolution fusion module(DFM)is designed to enhance the expression of feature information.The balanced feature pyramid(BFP)is embedded to aggregate the global feature.The feature refinement is constructed to adjust the original feature maps by considering the feature alignment between the anchors and devices.It can generate more accurate feature vectors.To address the problem of sample imbalance between electrical devices,the gradient harmonized mechanism(GHM)loss is utilized to adjust the weight of each sample.The ablation experiments are conducted on the SRDD dataset.F3RNet achieves the best detection performance compared with classical object detection networks.Also,it is verified that the features from global feature maps can effectively recognize the similar and small devices.展开更多
Due to factors such as motion blur,video out-of-focus,and occlusion,multi-frame human pose estimation is a challenging task.Exploiting temporal consistency between consecutive frames is an efficient approach for addre...Due to factors such as motion blur,video out-of-focus,and occlusion,multi-frame human pose estimation is a challenging task.Exploiting temporal consistency between consecutive frames is an efficient approach for addressing this issue.Currently,most methods explore temporal consistency through refinements of the final heatmaps.The heatmaps contain the semantics information of key points,and can improve the detection quality to a certain extent.However,they are generated by features,and feature-level refinements are rarely considered.In this paper,we propose a human pose estimation framework with refinements at the feature and semantics levels.We align auxiliary features with the features of the current frame to reduce the loss caused by different feature distributions.An attention mechanism is then used to fuse auxiliary features with current features.In terms of semantics,we use the difference information between adjacent heatmaps as auxiliary features to refine the current heatmaps.The method is validated on the large-scale benchmark datasets PoseTrack2017 and PoseTrack2018,and the results demonstrate the effectiveness of our method.展开更多
基金supported by the National Natural Science Foundation of China(No.62203163)the Scientific Research Project of Hunan Provincial Education Department(No.24A0519)+1 种基金the Hunan Provincial Natural Science Foundation(No.2025JJ60407)the Postgraduate Scientific Research Innovation Project of Hunan Province(No.CX2024100).
文摘Accurate detection of smoke and fire sources is critical for early fire warning and environmental monitoring.However,conventional detection approaches are highly susceptible to noise,illumination variations,and complex environmental conditions,which often reduce detection accuracy and real-time performance.To address these limitations,we propose Lightweight and Precise YOLO(LP-YOLO),a high-precision detection framework that integrates a self-attention mechanism with a feature pyramid,built upon YOLOv8.First,to overcome the restricted receptive field and parameter redundancy of conventional Convolutional Neural Networks(CNNs),we design an enhanced backbone based on Wavelet Convolutions(WTConv),which expands the receptive field through multifrequency convolutional processing.Second,a Bidirectional Feature Pyramid Network(BiFPN)is employed to achieve bidirectional feature fusion,enhancing the representation of smoke features across scales.Third,to mitigate the challenge of ambiguous object boundaries,we introduce the Frequency-aware Feature Fusion(FreqFusion)module,in which the Adaptive Low-Pass Filter(ALPF)reduces intra-class inconsistencies,the offset generator refines boundary localization,and the Adaptive High-Pass Filter(AHPF)recovers high-frequency details lost during down-sampling.Experimental evaluations demonstrate that LP-YOLO significantly outperforms the baseline YOLOv8,achieving an improvement of 9.3%in mAP@50 and 9.2%in F1-score.Moreover,the model is 56.6%and 32.4%smaller than YOLOv7-tiny and EfficientDet,respectively,while maintaining real-time inference speed at 238 frames per second(FPS).Validation on multiple benchmark datasets,including D-Fire,FIRESENSE,and BoWFire,further confirms its robustness and generalization ability,with detection accuracy consistently exceeding 82%.These results highlight the potential of LP-YOLO as a practical solution with high accuracy,robustness,and real-time performance for smoke and fire source detection.
基金Supported by the National Natural Science Foundation of China（60802061, 11426087） Supported by Key Project of Science and Technology of the Education Department Henan Province（14A120009）+1 种基金 Supported by the Program of Henan Province Young Scholar（2013GGJS-027） Supported by the Research Foundation of Henan University（2013YBZR016）
文摘Two key challenges raised by a product images classification system are classification precision and classification time. In some categories, classification precision of the latest techniques, in the product images classification system, is still low. In this paper, we propose a local texture descriptor termed fan refined local binary pattern, which captures more detailed information by integrating the spatial distribution into the local binary pattern feature. We compare our approach with different methods on a subset of product images on Amazon/e Bay and parts of PI100 and experimental results have demonstrated that our proposed approach is superior to the current existing methods. The highest classification precision is increased by 21% and the average classification time is reduced by 2/3.
基金Supported by the Ph.D. Research Startup Project of Minnan Normal University(KJ2021020)the National Natural Science Foundation of China(12090020 and 12090025)Zhejiang Provincial Natural Science Foundation of China(LSD19H180005)。
文摘Accurate pancreas segmentation is critical for the diagnosis and management of diseases of the pancreas. It is challenging to precisely delineate pancreas due to the highly variations in volume, shape and location. In recent years, coarse-to-fine methods have been widely used to alleviate class imbalance issue and improve pancreas segmentation accuracy. However,cascaded methods could be computationally intensive and the refined results are significantly dependent on the performance of its coarse segmentation results. To balance the segmentation accuracy and computational efficiency, we propose a Discriminative Feature Attention Network for pancreas segmentation, to effectively highlight pancreas features and improve segmentation accuracy without explicit pancreas location. The final segmentation is obtained by applying a simple yet effective post-processing step. Two experiments on both public NIH pancreas CT dataset and abdominal BTCV multi-organ dataset are individually conducted to show the effectiveness of our method for 2 D pancreas segmentation. We obtained average Dice Similarity Coefficient(DSC) of 82.82±6.09%, average Jaccard Index(JI) of 71.13± 8.30% and average Symmetric Average Surface Distance(ASD) of 1.69 ± 0.83 mm on the NIH dataset. Compared to the existing deep learning-based pancreas segmentation methods, our experimental results achieve the best average DSC and JI value.
文摘This paper presents a novel approach for camera pose refinement based on neural radiance fields(NeRF)by introducing semantic feature consistency to enhance robustness.NeRF has been successfully applied to camera pose estimation by inverting the rendering process given an observed RGB image and an initial pose estimate.However,previous methods only adopted photometric consistency for pose optimization,which is prone to be trapped in local minima.To address this problem,we introduce semantic feature consistency into the existing framework.Specifically,we utilize high-level features extracted from a convolutional neural network(CNN)pre-trained for image recognition,and maintain consistency of such features between observed and rendered images during the optimization procedure.Unlike the color values at each pixel,these features contain rich semantic information shared within local regions and can be more robust to appearance changes from different viewpoints.Since it is computationally expensive to render a full image with NeRF for feature extraction from CNN,we propose an efficient way to estimate the features of individually rendered pixels by projecting them to a nearby reference image and interpolating its feature maps.Extensive experiments show that our method greatly outperforms the baseline method on both synthetic objects and real-world large indoor scenes,increasing the accuracy of pose estimation by over 6.4%.
文摘In the research of software reuse, feature models have been widely adopted to capture, organize and reuse the requirements of a set of similar applications in a software do- main. However, the construction, especially the refinement, of feature models is a labor-intensive process, and there lacks an effective way to aid domain engineers in refining feature models. In this paper, we propose a new approach to support interactive refinement of feature models based on the view updating technique. The basic idea of our approach is to first extract features and relationships of interest from a possibly large and complicated feature model, then organize them into a comprehensible view, and finally refine the feature model through modifications on the view. The main characteristics of this approach are twofold： a set of powerful rules （as the slicing criterion） to slice the feature model into a view auto- matically, and a novel use of a bidirectional transformation language to make the view updatable. We have successfully developed a tool, and a nontrivial case study shows the feasi- bility of this approach.
基金funded by the National Natural Science Foundation of China(Grant No.52072408),author Y.C.
文摘Onboard visual object tracking in unmanned aerial vehicles(UAVs)has attractedmuch interest due to its versatility.Meanwhile,due to high precision,Siamese networks are becoming hot spots in visual object tracking.However,most Siamese trackers fail to balance the tracking accuracy and time within onboard limited computational resources of UAVs.To meet the tracking precision and real-time requirements,this paper proposes a Siamese dense pixel-level network for UAV object tracking named SiamDPL.Specifically,the Siamese network extracts features of the search region and the template region through a parameter-shared backbone network,then performs correlationmatching to obtain the candidate regionwith high similarity.To improve the matching effect of template and search features,this paper designs a dense pixel-level feature fusion module to enhance the matching ability by pixel-wise correlation and enrich the feature diversity by dense connection.An attention module composed of self-attention and channel attention is introduced to learn global context information and selectively emphasize the target feature region in the spatial and channel dimensions.In addition,a target localization module is designed to improve target location accuracy.Compared with other advanced trackers,experiments on two public benchmarks,which are UAV123@10fps and UAV20L fromthe unmanned air vehicle123(UAV123)dataset,show that SiamDPL can achieve superior performance and low complexity with a running speed of 100.1 fps on NVIDIA TITAN RTX.
基金This work was supported by Science and Technology Project of State Grid Corporation of China(Research and application of audiovisual active perception and collaborative cognitive technology for smart grid operation and maintenance scenarios)(5600–202046347 A-0–0–00).
文摘In modern energy systems,substations are the core of electricity transmission and distribution.However,similar appearance and small size pose significant challenges for automatic identification of electrical devices.To address these issues,we collect and annotate the substation rotated device dataset(SRDD).Further,feature fusion and feature refinement network(F3RNet)are constructed based on the classic structure pattern of backbone-neck-head.Considering the similar appearance of electrical devices,the deconvolution fusion module(DFM)is designed to enhance the expression of feature information.The balanced feature pyramid(BFP)is embedded to aggregate the global feature.The feature refinement is constructed to adjust the original feature maps by considering the feature alignment between the anchors and devices.It can generate more accurate feature vectors.To address the problem of sample imbalance between electrical devices,the gradient harmonized mechanism(GHM)loss is utilized to adjust the weight of each sample.The ablation experiments are conducted on the SRDD dataset.F3RNet achieves the best detection performance compared with classical object detection networks.Also,it is verified that the features from global feature maps can effectively recognize the similar and small devices.
基金supported by the National Key Research and Development Program of China(Nos.2021YFC2009200 and 2023YFC3606100)the Special Project of Technological Innovation and Application Development of Chongqing,China(No.cstc2019jscx-msxmX0167)。
文摘Due to factors such as motion blur,video out-of-focus,and occlusion,multi-frame human pose estimation is a challenging task.Exploiting temporal consistency between consecutive frames is an efficient approach for addressing this issue.Currently,most methods explore temporal consistency through refinements of the final heatmaps.The heatmaps contain the semantics information of key points,and can improve the detection quality to a certain extent.However,they are generated by features,and feature-level refinements are rarely considered.In this paper,we propose a human pose estimation framework with refinements at the feature and semantics levels.We align auxiliary features with the features of the current frame to reduce the loss caused by different feature distributions.An attention mechanism is then used to fuse auxiliary features with current features.In terms of semantics,we use the difference information between adjacent heatmaps as auxiliary features to refine the current heatmaps.The method is validated on the large-scale benchmark datasets PoseTrack2017 and PoseTrack2018,and the results demonstrate the effectiveness of our method.
