Bolt Defect Detection Method Based on Multiple Attention and Feature Alignment
The rapid expansion of power grid had higher requirements for the detection and maintenance of transmission lines,thus intelligent and efficient automated power inspection algorithms become one of the important research directions.In order to accurately identify the defective bolts in power line inspection image,we proposed a bolt defect detection method based on multiple attention and feature alignment.Firstly,we built a Deformable-DETR(deformable detection transformer)framework based on deformable attention to solve the problem that existing power inspection algorithms could not model the pixel contour-environment relationship through convolutional neural networks.Secondly,we proposed a proposal based local attention module to alleviate the problem of insufficient feature granularity caused by deformable attention.Thirdly,in order to enrich the object feature quality under the existing data,we proposed an object-level based feature alignment module based and a constraint functions.Finally,drone inspection image of a power company in central China were selected for verification.The experimental results show that the overall performance of the proposed algorithm improves by 5.4%compared with the existing convolutional algorithms,and the overall mean average precision reaches 90.5%.
transmission line fault detectiondrone inspectionattention mechanismobject detectiondeep learning
国家科技期刊平台
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