L-YOLOv8s: an improved YOLOv8s based on lightweight for defect insulator detection

To enhance the speed and accuracy of insulator detection in unmanned aerial vehicle aerial photography, this study introduces a lightweight network, L-YOLOv8s, based on you only look once v8 (YOLOv8), designed for real-time detection of insulators and their defects. Initially, a lightweight backbone network, MobileNetv3-ECA-SPPF, is proposed. This network is capable of reducing the model parameter redundancy and improving the detection speed. The Slim-Neck module is employed to enhance the feature fusion component of YOLOv8s, thereby reducing the computational load and network complexity while maintaining model accuracy. For the precise recognition of small targets such as breakages and flash contamination, the wise intersection over the union v3 edge loss function is introduced to replace the original distance intersection over the union edge loss function, which is more beneficial for the model’s efficiency in recognizing small targets. Experimental results demonstrate that L-YOLOv8s can accurately and swiftly identify various types of insulators and their defects. The model’s accuracy reaches 94.8%, with a recognition speed of 454.55 frames per second. The number of model parameters is only 3.37M, and the floating-point operation is 7.2 Giga floating-point operations per second. Compared with the YOLOv8 model, the accuracy and floating-point operation of L-YOLOv8s are higher by 2.5%, the recognition speed is improved by 59.09%, the number of model parameters is reduced by 69.69%, and the floating-point operation is decreased by 74.65%. When compared with several traditional models, L-YOLOv8s proves to be practically significant in the identification of insulators and their defects.