改进YOLOv7x检测电力设备缺陷，精度达97%
Improved YOLOv7x-Based Defect Detection Algorithm for Power Equipment
- 引入ACmix与Biformer模块，增强特征提取与关键信息聚焦
- 用MPDIoU替代原损失函数，提升定位准确性，[email protected]达到93.5%
- 适合电力巡检、工业质检等高精度缺陷检测场景
电力设备的正常运行对电力系统至关重要，因此其异常检测意义重大。本文提出一种改进的基于YOLOv7x的电力设备异常检测算法。首先，引入ACmix卷积混合注意力模块，有效抑制背景噪声和无关特征，提升网络特征提取能力；其次，在网络中加入Biformer注意力机制，强化对关键特征的关注，提高模型对特征图像的灵活识别能力；最后，为更全面评估预测框与真实框的关系，将原损失函数替换为MPDIoU函数，解决了预测框不匹配的问题。改进算法显著提升检测精度，所有目标类别的[email protected]达到93.5%，精确率为97.1%，召回率为97%。
原文摘要 · Abstract (English)
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%.
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