Detection methods for foreign objects in transmission tower poles based on improved YoLov4-tiny

Abstract

At present, a large number of light UAV is used in power patrol instead of traditional manual patrol to improve work efficiency, and a large number of detection equipment need to be equipped with lightweight detection algorithm with excellent performance. To solve this problem, an improved YoLov4-tiny algorithm is proposed. The main structural improvement is to add an improved SPP structure to the output layer of the backbone network. Because of the limited sample, the network is trained by the idea of transfer learning, and the mixed data enhancement technique is used to expand the sample of data set. Compared with YoLoV4, the weight file memory is only 28.8%, the memory is only 48.1 MB, mAP is 10.65%, and the improved model is 97.06%.