Enhanced YOLOv8 algorithm for large-scale multi-object detection and its application in defect detection in power systems

Defect detection in power systems is crucial for ensuring the safety, stability, and reliable operation of these systems. However, traditional image recognition algorithms face challenges such as low accuracy, slow speed, and poor generalization when dealing with large-scale multi-object detection tasks. This paper proposes an enhanced YOLOv8 algorithm for large-scale multi-object detection and applies it to defect detection in power systems. The proposed method introduces attention mechanisms, multi-scale feature fusion, and adaptive anchor box generation techniques based on the YOLOv8 algorithm, thereby improving the model's detection accuracy and robustness. Experiments were conducted on a publicly available power system image datasets and compared with algorithms such as YOLOv8, YOLOv5, and Faster R-CNN. The experimental results demonstrate that the proposed method has a significant advantage in large-scale multi-object detection, achieving a mean average precision (mAP) of 90.2.