Detection of Key Components of Transmission Lines Based on the Improved YOLOv8

The detection of key components in transmission lines faces challenges such as significant variations in object scales, complex backgrounds, and difficulties in detecting small targets, leading to low detection accuracy and missed detections. In this study, we propose an improved YOLOv8 algorithm for detecting key components of transmission lines. First, we incorporate Deformable Convolution (DCNv3) to improve the backbone network's feature extraction capability and mitigate accuracy degradation caused by occlusion and angle variations. Subsequently, we use the Adaptively Spatial Feature Fusion (ASFF) to progressively fuse features of different scales and add same-layer skip connections, enabling efficient feature fusion and strengthening the model's ability to detect small objects. Finally, we replaced the original CIoU with the RIoU to further boost the detection capability for small objects. The modified algorithm achieves an mAP<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50</inf> of 96.5%, an mAP<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50:95</inf> of 79.2%, representing a 3.1% and 4.8% improvement compared to the original YOLOv8 model.