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To keep the balance of precision and speed of unmanned aerial vehicles (UAVs) in detecting insulator defects during power inspection, an improved insulator defect identification algorithm, Insu-YOLO, which is based on the latest YOLOv8 network, is proposed in this paper. Firstly, to lower the computational complexity of the network, the GSConv module is introduced in the backbone and neck network. In the neck network, a lightweight content-aware reassembly of features (CARAFE) structure is adopted to better utilize the feature information for upsampling, which enhances the feature fusion capability of Insu-YOLO. Additionally, Insu-YOLO enhances the fusion between shallow and deep feature maps by adding an extra object detection layer, thereby increasing the accuracy for detecting small targets. The experimental results indicate that the mean average precision of Insu-YOLO reaches 95.9%, which is 3.95% higher than the YOLOv8n baseline model, with a memory usage of 9.2 MB. Moreover, the detection speed of Insu-YOLO is 87 frames/s which achieves the purpose of real-time identification of insulator defects.
Chen et al. (Tue,) studied this question.