Artificial intelligence has demonstrated notable advancements in the realm of visual inspection and defect detection in substations. Nevertheless, practical application presents challenges, with issues arising from the dynamic shooting environment and limited dataset resulting in suboptimal defect identification accuracy and instability. To address these concerns, a pioneering approach based on hybrid pruning YOLOv5 and multiscale data augmentation is proposed for enhancing defect detection in substations. Initially, an enhanced multiscale data augmentation method is proposed. The improved multiscale data augmentation mitigates the impact of the time-varying shooting environment on recognition accuracy and enhances defect detection precision. Subsequently, YOLOv5 is employed for training and detecting defects within multi-scale image data. To alleviate the potential destabilizing effects of YOLOv5's large-scale parameters on model stability, a new model pruning method is implemented. This method strategically prunes parameters to bolster the model's defect identification accuracy. The efficacy of the proposed methodology is evaluated through testing on substation defect images, confirming its effectiveness in enhancing defect detection capabilities.
Keywords: Defect detection; Model pruning; Multiscale data augmentation; Substation; YOLOv5.
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