Research on an insulator defect detection method based on LSKNet
Currently,power network defect detection is mainly accomplished by UAV(Unmanned Aerial Vehicles)aerial photogra-phy.Screening the current publicly available dataset,it is found that the labeling error of insulators is large and the positive and nega-tive samples are out of balance;At the same time,there are many small-scaled and slender-type targets in the inspection images,and it is difficult to achieve high-precision detection using the existing algorithms.To address the above problems,a new dataset is con-structed by fogging algorithm,a large selective kernel network(LSKNet)is used,and the Dark Channel Prior algorithm is introduced to propose a LSK insulator image defogging algorithm for defects in power networks.The experimental results show that the mAP on the SFID-PRO dataset reaches 85.90%,in which the recall rate of defective insulators reaches 99.6%,and it is able to accurately detect elongated objects and small-sized objects.
Small target detectionInsulator defectsDark channel priorLSKNetDeep learning
国家科技期刊平台
NSTL
万方数据
维普
