YOLOv10n-Based Defect Detection in Power Insulators: Attention Enhancement and Feature Fusion Optimization

In modern power systems, insulators, as key components of transmission lines, are crucial for defect detection for the safe operation of power grids. Aiming at the problems of low efficiency of traditional manual detection, the vulnerability of traditional image processing methods to environmental interference, and the insufficient ability of existing deep learning models to detect small target defects under complex backgrounds, this paper proposes an improved target detection model based on YOLOv10n, which is the first time to integrate the spatial channel attention mechanism (SEAttention) with the up-sampling expansion operation (Patch Expanding) in the Neck part of the model. In the Neck part of the model, the spatial channel attention mechanism (SEAttention) and the up-sampling expansion operation (Patch Expanding) are deeply integrated. The spatial information of the channels is compressed by global average pooling, and the feature map is adaptively weighted after learning the channel weights through the fully connected layer, so as to strengthen the key channel features and suppress the noise. The Concat module in Neck is replaced by a two-way weighted feature fusion mechanism, which constructs a top-down and bottom-up two-way information flow, reduces the loss of cross-layer information, and significantly enhances the model’s ability to detect multi-scale defects, such as insulator cracks and fouling, and especially improves the resolution of details of small targets in long-distance shooting scenarios. The channel-space dual attention mechanism (CBAM) is integrated into the C2f module, and the channel weights are computed by global average pooling and maximum pooling in parallel, combined with the spatial attention features extracted by convolution, to realize the accurate focusing on defect-related channels and spatial regions, and the experimental results show that the improved model’s mAP@50 reaches 94.2%, which is 2.4% better than that of the baseline model YOLOv10n, and better than that of YOLOv5n. The experimental results show that the mAP@50 of the improved model reaches 94.2%, which is 2.4% higher than that of the baseline model YOLOv10n, and better than the mainstream algorithms such as YOLOv5 (93.1%) and YOLOv8n (93.2%).