To address the complexity of defect types, variability in target scales, and background interference in power equipment within substations, a deep feature perception method for substation equipment defect detection is proposed. Firstly, a C2f+ module based on a sliding window mechanism is designed to enable the backbone network to effectively handle cross-window feature information, expand the network′s receptive field, and strengthen the network′s deep attention to local features and global feature processing capabilities. Next, SimAM is integrated into the feature fusion stage of the network, empowering the network with the ability to autonomously learn and dynamically fine-tune attention weights to improve perceptual performance for multi-scale defects. Additionally, to mitigate detection errors introduced by defects of varying sizes and shapes, a fusion loss function combining NWD and CIOU is designed. By modeling the predicted and actual bounding boxes as two-dimensional Gaussian distributions, this enhances the model′s learning ability for defect localization. Experimental results on a dataset of substation equipment defects demonstrate that the proposed model achieves an average detection accuracy （mAP） of 98.27 % across 11 defect categories. It also outperforms six mainstream target detection methods and power equipment defect detection methods with the best accuracy in 9 out of 11 defect detections. At the same time, the model achieves a frame rate （FPS） of 78.4, demonstrating high real-time performance.