To maintain the reliability of power systems,routine inspections using drones equipped with advanced object detection algorithms are essential for preempting power-related issues.The increasing resolution of drone-cap...To maintain the reliability of power systems,routine inspections using drones equipped with advanced object detection algorithms are essential for preempting power-related issues.The increasing resolution of drone-captured images has posed a challenge for traditional target detection methods,especially in identifying small objects in high-resolution images.This study presents an enhanced object detection algorithm based on the Faster Regionbased Convolutional Neural Network(Faster R-CNN)framework,specifically tailored for detecting small-scale electrical components like insulators,shock hammers,and screws in transmission line.The algorithm features an improved backbone network for Faster R-CNN,which significantly boosts the feature extraction network’s ability to detect fine details.The Region Proposal Network is optimized using a method of guided feature refinement(GFR),which achieves a balance between accuracy and speed.The incorporation of Generalized Intersection over Union(GIOU)and Region of Interest(ROI)Align further refines themodel’s accuracy.Experimental results demonstrate a notable improvement in mean Average Precision,reaching 89.3%,an 11.1%increase compared to the standard Faster R-CNN.This highlights the effectiveness of the proposed algorithm in identifying electrical components in high-resolution aerial images.展开更多
Tricuspid regurgitation(TR)is an often encountered but undertreated valvular heart lesion.Unlike left-sided valvular diseases,it is estimated that only 5%of patients with significant TR were treated surgically,among w...Tricuspid regurgitation(TR)is an often encountered but undertreated valvular heart lesion.Unlike left-sided valvular diseases,it is estimated that only 5%of patients with significant TR were treated surgically,among which 86%were treated in combination with other major procedures.[1]This unmet need partially resulted from the lack of prospective evidence on reduced mortality of severe TR benefiting from surgery,alongside a historical viewpoint that functional TR(being the etiology for the vast majority of TR)would be improved once problems in the left heart are corrected.However,untreated severe TR shows various symptoms and a poor prognosis,including increased early and late mortality and impaired quality of life.[2]With the rapid development of catheter-based valve therapies,less invasive treatment options have been offered to the once forgotten tricuspid valve,further expanding the complexity of decision-making for TR intervention.展开更多
基金supported by the Shanghai Science and Technology Innovation Action Plan High-Tech Field Project(Grant No.22511100601)for the year 2022 and Technology Development Fund for People’s Livelihood Research(Research on Transmission Line Deep Foundation Pit Environmental Situation Awareness System Based on Multi-Source Data).
文摘To maintain the reliability of power systems,routine inspections using drones equipped with advanced object detection algorithms are essential for preempting power-related issues.The increasing resolution of drone-captured images has posed a challenge for traditional target detection methods,especially in identifying small objects in high-resolution images.This study presents an enhanced object detection algorithm based on the Faster Regionbased Convolutional Neural Network(Faster R-CNN)framework,specifically tailored for detecting small-scale electrical components like insulators,shock hammers,and screws in transmission line.The algorithm features an improved backbone network for Faster R-CNN,which significantly boosts the feature extraction network’s ability to detect fine details.The Region Proposal Network is optimized using a method of guided feature refinement(GFR),which achieves a balance between accuracy and speed.The incorporation of Generalized Intersection over Union(GIOU)and Region of Interest(ROI)Align further refines themodel’s accuracy.Experimental results demonstrate a notable improvement in mean Average Precision,reaching 89.3%,an 11.1%increase compared to the standard Faster R-CNN.This highlights the effectiveness of the proposed algorithm in identifying electrical components in high-resolution aerial images.
基金supported by the National Natural Science Foundation of China(Nos.81970325 and 82102129).
文摘Tricuspid regurgitation(TR)is an often encountered but undertreated valvular heart lesion.Unlike left-sided valvular diseases,it is estimated that only 5%of patients with significant TR were treated surgically,among which 86%were treated in combination with other major procedures.[1]This unmet need partially resulted from the lack of prospective evidence on reduced mortality of severe TR benefiting from surgery,alongside a historical viewpoint that functional TR(being the etiology for the vast majority of TR)would be improved once problems in the left heart are corrected.However,untreated severe TR shows various symptoms and a poor prognosis,including increased early and late mortality and impaired quality of life.[2]With the rapid development of catheter-based valve therapies,less invasive treatment options have been offered to the once forgotten tricuspid valve,further expanding the complexity of decision-making for TR intervention.
