Power equipment target detection algorithm based on improved YOLOV7
Jinglong Yang, Xiu Ji, Peng Cheng Wu, M X Li · 2024
Aiming at some practical problems such as low inspection efficiency and high false detection rate of traditional manual transmission lines, we introduce an enhanced YOLOV7-based algorithm for infrared insulator target detection. The algorithm uses K-Means ++ clustering algorithm to regenerate the anchor frame on the self-made infrared insulator data set, which makes the algorithm converge faster. The algorithm substitutes the original YOLOV7 backbone with Efficientformerv2 to simplify the backbone network. Additionally, it eliminates redundant information from the feature map by incorporating the GhostConv module in the feature fusion network, and introducing the attention mechanism called SimAM. WIoU is used as the loss function of the improved model. Experiments on self-made infrared insulator data set show that when compared to the initial YOLOV7 algorithm, the refined algorithm demonstrates notable enhancements across multiple performance indicators. Particularly, the [email protected] metric has seen a substantial improvement of 5.2%, elevating it to an impressive 98.3%. Concurrently, the number of parameters has experienced a significant reduction of 43.5%, dwindling to 20.5 million, and the computational workload has been markedly lowered by 66.2% to 35.0 GFPLOS.