Focused on the task of fast and accurate armored target detection in ground battlefield,a detection method based on multi-scale representation network(MS-RN) and shape-fixed Guided Anchor(SF-GA)scheme is proposed.Firs...Focused on the task of fast and accurate armored target detection in ground battlefield,a detection method based on multi-scale representation network(MS-RN) and shape-fixed Guided Anchor(SF-GA)scheme is proposed.Firstly,considering the large-scale variation and camouflage of armored target,a new MS-RN integrating contextual information in battlefield environment is designed.The MS-RN extracts deep features from templates with different scales and strengthens the detection ability of small targets.Armored targets of different sizes are detected on different representation features.Secondly,aiming at the accuracy and real-time detection requirements,improved shape-fixed Guided Anchor is used on feature maps of different scales to recommend regions of interests(ROIs).Different from sliding or random anchor,the SF-GA can filter out 80% of the regions while still improving the recall.A special detection dataset for armored target,named Armored Target Dataset(ARTD),is constructed,based on which the comparable experiments with state-of-art detection methods are conducted.Experimental results show that the proposed method achieves outstanding performance in detection accuracy and efficiency,especially when small armored targets are involved.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
基金supported by the National Key Research and Development Program of China under grant 2016YFC0802904National Natural Science Foundation of China under grant61671470the Postdoctoral Science Foundation Funded Project of China under grant 2017M623423。
文摘Focused on the task of fast and accurate armored target detection in ground battlefield,a detection method based on multi-scale representation network(MS-RN) and shape-fixed Guided Anchor(SF-GA)scheme is proposed.Firstly,considering the large-scale variation and camouflage of armored target,a new MS-RN integrating contextual information in battlefield environment is designed.The MS-RN extracts deep features from templates with different scales and strengthens the detection ability of small targets.Armored targets of different sizes are detected on different representation features.Secondly,aiming at the accuracy and real-time detection requirements,improved shape-fixed Guided Anchor is used on feature maps of different scales to recommend regions of interests(ROIs).Different from sliding or random anchor,the SF-GA can filter out 80% of the regions while still improving the recall.A special detection dataset for armored target,named Armored Target Dataset(ARTD),is constructed,based on which the comparable experiments with state-of-art detection methods are conducted.Experimental results show that the proposed method achieves outstanding performance in detection accuracy and efficiency,especially when small armored targets are involved.
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.
