A Defect Detection Method for Substation Equipment Based on Image Data Generation and Deep Learning

The task of detecting surface defects on substation equipment faces several challenges, including a variety of target categories, the scarcity of original defect image data, complex environmental conditions, low accuracy in existing algorithms, as well as notable issues with false alarms and missed detections. Overcoming these obstacles is crucial for the successful implementation of intelligent inspection systems for substations. To address the problem of limited original data, we first employ the method of ADD-GAN to augment the image training set. Furthermore, this paper proposes a target detection model called YOLO-SD to detect various equipment defects in complex real-world scenarios. In order to enhance the network's feature extraction capabilities in the presence of complex backgrounds and to improve detection accuracy, a novel deep perceptual feature extraction module named C3+ was introduced in this research. Furthermore, we incorporates SimAM into the neck network of YOLO-SD. This integration not only bolsters the network's learning capacity but also equips it with the capability to autonomously learn and dynamically fine-tune attention weights to suit different input scenarios. To tackle the challenges posed by variations in size and shapes of different substation equipment defects in images, a novel fusion loss function NWD-CIoU is designed. The improvements enhance the accuracy and robustness of YOLO-SD in defect target detection across different scales. The experiment demonstrated that the YOLO-SD model achieved an mAP@0.5 of 90.3% and mAP@0.5:0.95 of 63.9% in detecting defects in substation equipment. The F1 score reached 81.1%, IoU value was 90.5%. This model realized accurate detection of multi-scale substation defect targets, reaching the state-of-the-art level in substation defects detection.