基于改进YoloX-s的密贴检查器故障检测方法

为了降低密贴检查器维护和检修中的高运营成本，提高安全保障能力，提出了一种改进YoloX-s的密贴检查器故障检测方法。通过改进YoloX-s中的PANet路径融合网络，进一步地增加了与浅层特征层的融合；此外，增加了CA（Coordinate Attention）注意力机制，将注意力集中在目标区域内，以获取细节信息；选用CIoU损失函数以聚焦目标框与检测框之间的重叠面积、中心点距离和长宽比，提高模型的定位精度。实验结果表明，相较于YoloX-s模型，所提模型有着更好的综合表现，动接点环平均精度为97.73%，静接点片平均精度为98.83%，平均精度均值为98.28%。

A fault detection method of the closure detectors based on the improved YoloX-s

In order to reduce a high operation cost of maintenance and recondition, as well as improving the security capability, we employed an improved YoloX-s detection method for the fault of closure detectors. By elevating the PANet path fusion network of the proposed model, a fusion with shallow feature layer is further strengthened; In addition, we added the CA(Coordinate Attention) attention mechanism to the model for the more detailed information in the target area. Moreover, the CIoU loss function is selected to enhance a positioning accuracy, which is aimed at the overlapping area, the center point distance and the aspect ratio between a target frame and a detection frame. After various tests, the experimental results showed that the presented model has a better comprehensive performance compared with the existing YoloX-s model. Furthermore, an average accuracy of moving contact reached 97.73%, an average accuracy of static contact reached 98.83%, and an average accuracy reached 98.28%.