LA_YOLOx: Effective Model to Detect the Surface Defects of Insulative Baffles

Abstract

In the field of industry, defect detection based on YOLO models is widely used. In real detection, the method of defect detection of insulative baffles is artificial detection. The work efficiency of this method, however, is low because the detection is depends absolutely on human eyes. Considering the excellent performance of YOLOx, an intelligent detection method based on YOLOx is proposed. First, we selected a CIOU loss function instead of an IOU loss function by analyzing the defect characteristics of insulative baffles. In addition, considering the limitation of model resources in application scenarios, the lightweight YOLOx model is proposed. We replaced YOLOx’s backbone with lightweight backbones (MobileNetV3 and GhostNet), and used Depthwise separable convolution instead of conventional convolution. This operation reduces the number of network parameters by about 42% compared with the original YOLOx network. However, the mAP of it is decreased by about 0.8% compared with the original YOLOx model. Finally, the attention mechanism is introduced into the feature fusion module to solve this problem, and we called the lightweight YOLOx with an attention module LA_YOLOx. The final value of mAP of LA_YOLOx reaches 95.60%, while the original YOLOx model is 95.31%, which proves the effectiveness of the LA_YOLOx model.

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