Rust Detection Network for Transmission Line Based on UAV Inspection

The target detection methods often utilise monitoring scenarios detected in power transmission line systems for defect detection. However, these conventional target detection techniques often falter when tasked with detecting diminutive targets akin to background elements in outdoor high-altitude power transmission line scenes. Such limitations compromise the efficacy of equipment defect detection. This research introduces a novel small target detection algorithm BP-YOLO, which leverages the BiFPN structure for bidirectional feature fusion and incorporates the BSAM attention mechanism to amplify the model's capacity to concentrate onminute target features. The BiFPN module concurrently processes feature maps across varying scales, facilitating effective network integration to extract pixel-level feature details of diminutive targets. Given that the P2 layer is typically employed for smaller target detection, we integrate the P2 small target layer with the BiFPN structure, yielding a higher resolution feature map and thereby augmenting the model's sensitivity to minuscule targets. Furthermore, the BSAM attention mechanism dynamically modulates the model's focus enabling adaptive concentration on regions containing small targets. In our proprietary transmission line small-size fitting dataset, the BP-YOLO algorithm registered an average accuracy of 88.3%, marking an improvement of 18.8% over theYOLOv8-m model and 35.2% over theYOLOv8 model. These findings underscore the proficiency of our proposed approach in identifying small-scale hardware anomalies in UAV inspection scenarios within power transmission lines.