Improved YOLOv7x-Based Defect Detection Algorithm for Power Equipment
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Abstract (arXiv)
The normal operation of power equipment plays a critical role in the power system, making anomaly detection for power equipment highly significant. This paper proposes an improved YOLOv7x-based anomaly detection algorithm for power equipment. First, the ACmix convolutional mixed attention mechanism module is introduced to effectively suppress background noise and irrelevant features, thereby enhancing the network's feature extraction capability. Second, the Biformer attention mechanism is added to the network to strengthen the focus on key features, improving the network's ability to flexibly recognize feature images. Finally, to more comprehensively evaluate the relationship between predicted and ground truth bounding boxes, the original loss function is replaced with the MPDIoU function, addressing the issue of mismatched predicted bounding boxes. The improved algorithm enhances detection accuracy, achieving a [email protected]/% of 93.5% for all target categories, a precision of 97.1%, and a recall of 97%.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 25 references that are in the map. First posted on arXiv 25 Feb 2025.
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References · 16
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- 2021On the Integration of Self-Attention and Convolutioncited by 74
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