YOLO-PowerLite: A Lightweight YOLO Model for Transmission Line Abnormal Target Detection

The secure and stable operation of power transmission lines is essential for electrical systems. Given that abnormal targets such as bird's nests and defective insulators may lead to transmission failures, timely detection of these targets is imperative. This paper introduces the YOLO-PowerLite model, an advanced lightweight object detection model based on YOLOv8n, designed for efficient, real-time detection on resource-constrained unmanned aerial vehicles (UAVs) equipped with edge computing platforms. In the feature fusion module, YOLO-PowerLite incorporates the innovative C2f_AK module, significantly reducing the number of parameters and enhancing the adaptability and fusion capability of features at different scales. Meanwhile, the adoption of the Bidirectional Feature Pyramid Network (BiFPN) further optimizes the efficiency and effectiveness of feature processing. In addition, the newly designed lightweight detection head significantly reduces the number of parameters and computational requirements. The integration of the Coordinate Attention mechanism in the backbone network enhances the model's ability to focus on and recognize abnormal targets in complex backgrounds. Experimental results show that YOLO-PowerLite achieves a mAP@0.5 of 94.2%, maintaining the accuracy of the original YOLOv8n while significantly reducing parameters, FLOPs, and model size by 42.3%, 30.9%, and 40.4%, respectively. Comparative analysis shows that YOLO-PowerLite surpasses other mainstream lightweight models in detection accuracy and computational efficiency. Deployment on the NVIDIA Jetson Xavier NX platform demonstrates an average processing time of 31.2 milliseconds per frame, highlighting its potential for real-time applications in monitoring transmission lines.