Considering the characteristics of deep thick top coal roadway,in which the high ground stress,coal seam with low strength,and a large range of surrounding rock fragmentation,the pressure relief anchor box beam suppor...Considering the characteristics of deep thick top coal roadway,in which the high ground stress,coal seam with low strength,and a large range of surrounding rock fragmentation,the pressure relief anchor box beam support system with high strength is developed.The high-strength bearing characteristics and coupling yielding support mechanism of this support system are studied by the mechanical tests of composite members and the combined support system.The test results show that under the coupling effect of support members,the peak stress of the box-shaped support beam in the anchor box beam is reduced by 21.9%,and the average deformation is increased by 135.0%.The ultimate bending bearing capacity of the box-shaped support beam is 3.5 times that of traditional channel beam.The effective compressive stress zone applied by the high prestressed cable is expanded by 26.4%.On this basis,the field support comparison test by the anchor channel beam,the anchor I-shaped beam and the anchor box beam are carried out.Compared with those of the previous two,the surrounding rock convergence of the latter is decreased by 41.2%and 22.2%,respectively.The field test verifies the effectiveness of the anchor box beam support system.展开更多
Robotic grasping is an essential problem at both the household and industrial levels,and unstructured objects have always been difficult for grippers.Parallel-plate grippers and algorithms,focusing on partial informat...Robotic grasping is an essential problem at both the household and industrial levels,and unstructured objects have always been difficult for grippers.Parallel-plate grippers and algorithms,focusing on partial information of objects,are one of the widely used approaches.However,most works predict single-size grasp rectangles for fixed cameras and gripper sizes.In this paper,a multi-scale grasp detector is proposed to predict grasp rectangles with different sizes on RGB-D or RGB images in real-time for hand-eye cameras and various parallel-plate grippers.The detector extracts feature maps of multiple scales and conducts predictions on each scale independently.To guarantee independence between scales and efficiency,fully matching model and background classifier are applied in the network.Based on analysis of the Cornell Grasp Dataset,the fully matching model canmatch all labeled grasp rectangles.Furthermore,background classification,along with angle classification and box regression,functions as hard negative mining and background predictor.The detector is trained and tested on the augmented dataset,which includes images of 320×320 pixels and grasp rectangles ranging from 20 tomore than 320 pixels.It performs up to 98.87% accuracy on image-wise dataset and 97.83% on object-wise split dataset at a speed of more than 22 frames per second.In addition,the detector,which is trained on a single-object dataset,can predict grasps on multiple objects.展开更多
Real-time and accurate traffic light status recognition can provide reliable data support for autonomous vehicle decision-making and control systems.To address potential problems such as the minor component of traffic...Real-time and accurate traffic light status recognition can provide reliable data support for autonomous vehicle decision-making and control systems.To address potential problems such as the minor component of traffic lights in the perceptual domain of visual sensors and the complexity of recognition scenarios,we propose an end-to-end traffic light status recognition method,ResNeSt50-CBAM-DINO(RC-DINO).First,we performed data cleaning on the Tsinghua-Tencent traffic lights(TTTL)and fused it with the Shanghai Jiao Tong University’s traffic light dataset(S2TLD)to form a Chinese urban traffic light dataset(CUTLD).Second,we combined residual network with split-attention module-50(ResNeSt50)and the convolutional block attention module(CBAM)to extract more significant traffic light features.Finally,the proposed RC-DINO and mainstream recognition algorithms were trained and analyzed using CUTLD.The experimental results show that,compared to the original DINO,RC-DINO improved the average precision(AP),AP at intersection over union(IOU)=0.5(AP50),AP for small objects(APs),average recall(AR),and balanced F score(F1-Score)by 3.1%,1.6%,3.4%,0.9%,and 0.9%,respectively,and had a certain capability to recognize the partially covered traffic light status.The above results indicate that the proposed RC-DINO improved recognition performance and robustness,making it more suitable for traffic light status recognition tasks.展开更多
Camouflaged targets are a type of nonsalient target with high foreground and background fusion and minimal target feature information,making target recognition extremely difficult.Most detection algorithms for camoufl...Camouflaged targets are a type of nonsalient target with high foreground and background fusion and minimal target feature information,making target recognition extremely difficult.Most detection algorithms for camouflaged targets use only the target’s single-band information,resulting in low detection accuracy and a high missed detection rate.We present a multimodal image fusion camouflaged target detection technique (MIF-YOLOv5) in this paper.First,we provide a multimodal image input to achieve pixel-level fusion of the camouflaged target’s optical and infrared images to improve the effective feature information of the camouflaged target.Second,a loss function is created,and the K-Means++clustering technique is used to optimize the target anchor frame in the dataset to increase camouflage personnel detection accuracy and robustness.Finally,a comprehensive detection index of camouflaged targets is proposed to compare the overall effectiveness of various approaches.More crucially,we create a multispectral camouflage target dataset to test the suggested technique.Experimental results show that the proposed method has the best comprehensive detection performance,with a detection accuracy of 96.5%,a recognition probability of92.5%,a parameter number increase of 1×10^(4),a theoretical calculation amount increase of 0.03 GFLOPs,and a comprehensive detection index of 0.85.The advantage of this method in terms of detection accuracy is also apparent in performance comparisons with other target algorithms.展开更多
Grasp detection is a visual recognition task where the robot makes use of its sensors to detect graspable objects in its environment.Despite the steady progress in robotic grasping,it is still difficult to achieve bot...Grasp detection is a visual recognition task where the robot makes use of its sensors to detect graspable objects in its environment.Despite the steady progress in robotic grasping,it is still difficult to achieve both real-time and high accuracy grasping detection.In this paper,we propose a real-time robotic grasp detection method,which can accurately predict potential grasp for parallel-plate robotic grippers using RGB images.Our work employs an end-to-end convolutional neural network which consists of a feature descriptor and a grasp detector.And for the first time,we add an attention mechanism to the grasp detection task,which enables the network to focus on grasp regions rather than background.Specifically,we present an angular label smoothing strategy in our grasp detection method to enhance the fault tolerance of the network.We quantitatively and qualitatively evaluate our grasp detection method from different aspects on the public Cornell dataset and Jacquard dataset.Extensive experiments demonstrate that our grasp detection method achieves superior performance to the state-of-the-art methods.In particular,our grasp detection method ranked first on both the Cornell dataset and the Jacquard dataset,giving rise to the accuracy of 98.9%and 95.6%,respectively at realtime calculation speed.展开更多
基金Project(2023YFC2907600)supported by the National Key Research and Development Program of ChinaProjects(42277174,42477166)supported by the National Natural Science Foundation of China+1 种基金Project(2024JCCXSB01)supported by the Fundamental Research Funds for the Central Universities,ChinaProject(KFJJ24-01M)supported by the State Key Laboratory of Explosion Science and Safety Protection,Beijing Institute of Technology,China。
文摘Considering the characteristics of deep thick top coal roadway,in which the high ground stress,coal seam with low strength,and a large range of surrounding rock fragmentation,the pressure relief anchor box beam support system with high strength is developed.The high-strength bearing characteristics and coupling yielding support mechanism of this support system are studied by the mechanical tests of composite members and the combined support system.The test results show that under the coupling effect of support members,the peak stress of the box-shaped support beam in the anchor box beam is reduced by 21.9%,and the average deformation is increased by 135.0%.The ultimate bending bearing capacity of the box-shaped support beam is 3.5 times that of traditional channel beam.The effective compressive stress zone applied by the high prestressed cable is expanded by 26.4%.On this basis,the field support comparison test by the anchor channel beam,the anchor I-shaped beam and the anchor box beam are carried out.Compared with those of the previous two,the surrounding rock convergence of the latter is decreased by 41.2%and 22.2%,respectively.The field test verifies the effectiveness of the anchor box beam support system.
基金fundings from Central Program of Basic Science of the National Natural Science Foundation of China(72088101)the National Postdoctoral Program for Innovative Talents(BX2021285).
文摘Robotic grasping is an essential problem at both the household and industrial levels,and unstructured objects have always been difficult for grippers.Parallel-plate grippers and algorithms,focusing on partial information of objects,are one of the widely used approaches.However,most works predict single-size grasp rectangles for fixed cameras and gripper sizes.In this paper,a multi-scale grasp detector is proposed to predict grasp rectangles with different sizes on RGB-D or RGB images in real-time for hand-eye cameras and various parallel-plate grippers.The detector extracts feature maps of multiple scales and conducts predictions on each scale independently.To guarantee independence between scales and efficiency,fully matching model and background classifier are applied in the network.Based on analysis of the Cornell Grasp Dataset,the fully matching model canmatch all labeled grasp rectangles.Furthermore,background classification,along with angle classification and box regression,functions as hard negative mining and background predictor.The detector is trained and tested on the augmented dataset,which includes images of 320×320 pixels and grasp rectangles ranging from 20 tomore than 320 pixels.It performs up to 98.87% accuracy on image-wise dataset and 97.83% on object-wise split dataset at a speed of more than 22 frames per second.In addition,the detector,which is trained on a single-object dataset,can predict grasps on multiple objects.
基金supported by the National Key R&D Program of China(2021YFB2501200)the Key Program of the National Natural Science Foundation of China(52131204)the Shaanxi Province Key Research and Development Program(2022GY-300).
文摘Real-time and accurate traffic light status recognition can provide reliable data support for autonomous vehicle decision-making and control systems.To address potential problems such as the minor component of traffic lights in the perceptual domain of visual sensors and the complexity of recognition scenarios,we propose an end-to-end traffic light status recognition method,ResNeSt50-CBAM-DINO(RC-DINO).First,we performed data cleaning on the Tsinghua-Tencent traffic lights(TTTL)and fused it with the Shanghai Jiao Tong University’s traffic light dataset(S2TLD)to form a Chinese urban traffic light dataset(CUTLD).Second,we combined residual network with split-attention module-50(ResNeSt50)and the convolutional block attention module(CBAM)to extract more significant traffic light features.Finally,the proposed RC-DINO and mainstream recognition algorithms were trained and analyzed using CUTLD.The experimental results show that,compared to the original DINO,RC-DINO improved the average precision(AP),AP at intersection over union(IOU)=0.5(AP50),AP for small objects(APs),average recall(AR),and balanced F score(F1-Score)by 3.1%,1.6%,3.4%,0.9%,and 0.9%,respectively,and had a certain capability to recognize the partially covered traffic light status.The above results indicate that the proposed RC-DINO improved recognition performance and robustness,making it more suitable for traffic light status recognition tasks.
基金Project supported by the Shandong Provincial Natural Science Foundation of China(No.ZR2020MF015)the Aerospace Science and Technology Innovation Institute Stabilization Support Project(No.ZY0110020009)。
文摘Camouflaged targets are a type of nonsalient target with high foreground and background fusion and minimal target feature information,making target recognition extremely difficult.Most detection algorithms for camouflaged targets use only the target’s single-band information,resulting in low detection accuracy and a high missed detection rate.We present a multimodal image fusion camouflaged target detection technique (MIF-YOLOv5) in this paper.First,we provide a multimodal image input to achieve pixel-level fusion of the camouflaged target’s optical and infrared images to improve the effective feature information of the camouflaged target.Second,a loss function is created,and the K-Means++clustering technique is used to optimize the target anchor frame in the dataset to increase camouflage personnel detection accuracy and robustness.Finally,a comprehensive detection index of camouflaged targets is proposed to compare the overall effectiveness of various approaches.More crucially,we create a multispectral camouflage target dataset to test the suggested technique.Experimental results show that the proposed method has the best comprehensive detection performance,with a detection accuracy of 96.5%,a recognition probability of92.5%,a parameter number increase of 1×10^(4),a theoretical calculation amount increase of 0.03 GFLOPs,and a comprehensive detection index of 0.85.The advantage of this method in terms of detection accuracy is also apparent in performance comparisons with other target algorithms.
基金supported by the National Key Research and Development Program of China under Grant No.2018AAA010-3002the National Natural Science Foundation of China under Grant Nos.62172392,61702482 and 61972379.
文摘Grasp detection is a visual recognition task where the robot makes use of its sensors to detect graspable objects in its environment.Despite the steady progress in robotic grasping,it is still difficult to achieve both real-time and high accuracy grasping detection.In this paper,we propose a real-time robotic grasp detection method,which can accurately predict potential grasp for parallel-plate robotic grippers using RGB images.Our work employs an end-to-end convolutional neural network which consists of a feature descriptor and a grasp detector.And for the first time,we add an attention mechanism to the grasp detection task,which enables the network to focus on grasp regions rather than background.Specifically,we present an angular label smoothing strategy in our grasp detection method to enhance the fault tolerance of the network.We quantitatively and qualitatively evaluate our grasp detection method from different aspects on the public Cornell dataset and Jacquard dataset.Extensive experiments demonstrate that our grasp detection method achieves superior performance to the state-of-the-art methods.In particular,our grasp detection method ranked first on both the Cornell dataset and the Jacquard dataset,giving rise to the accuracy of 98.9%and 95.6%,respectively at realtime calculation speed.
