CI-YOLO: A lightweight foreign object detection model for inspecting transmission line

Timely detection and elimination of foreign objects on transmission lines are of significance for the secure operation of power grids. With the widespread utilization of unmanned aerial vehicles (UAVs) for inspecting transmission lines, how to effectively balance the resource consumption and accuracy of detection model presents a challenge. To tackle this problem, this study proposes a foreign object detection model based on an improved YOLOv8n, realizing both lightweight and enhanced detection performance. Firstly, a SC Block structure is devised to substitute Bottleneck in C2f module of Backbone, minimizing the spatial and channel redundancy among features, enhancing the network performance, and reducing the consumption of computational resources. Subsequently, a novel dual attention mechanism, IEMA, is added after C2f module of Backbone, strengthening the crucial spatial and channel feature expression capability and significantly increasing the detection accuracy. Lastly, a new feature fusion network, CCFF_IEMA, is designed to replace Neck network, effectively integrating detailed features and reducing the parameter count and FLOPs. Experimental results indicate that in comparison to standard YOLOv8n, the parameters of our proposed CI-YOLO model are decreased by 40 %, FLOPs are reduced by 19.8 %, while achieving improvements of 0.7 % in Precision, 3.2 % in Recall, and 3.1 % in mAP0.5, respectively. The CI-YOLO model is beneficial to deployment on UAVs and provides a novel approach for detecting the foreign objects.