Exploring Adaptive Regression Loss and Feature Focusing in Industrial Scenarios

Industrial defect detection is designed to detect quality defects in industrial products. However, the surface defects of different industrial products vary greatly-for example, the variety of texture shapes and the complexity of background information. A lightweight Focus Encoder-Decoder Network (FEDNet) is presented to solve these problems. Specifically, the novelty of FEDNet is as follows: First, the feature focusing module (FFM) is designed to focus the attention on defect features in complex backgrounds. Secondly, a lightweight texture extraction module (LTEM) is proposed to lightly extract the texture and relative location information of shallow network defect features. Finally, the AZIoU, an adaptive adjustment loss function, is reexamined in the prediction box’s specific circumference and length-width bits. Experiments on two industrial defect datasets show that FEDNet achieves the accuracy of Steel at 42.86% and DeepPCB at 72.19% using only 15.3 GFLOPs.