LAUSR

Defect detection is a task to locate and classify the possible defects in an image. However, unlike common object detection tasks, defect detection often needs to deal with images with relatively complex backgrounds, for example, in industrial product quality inspection scenario. The complex background can greatly interfere with the feature of the target objects in the multiscale feature fusion process and therefore puts great challenge on the defect detector. In this work, a channel-space adaptive enhancement feature pyramid network (CA-FPN) is proposed to eliminate this interference from the complex background. By extracting the inner relationship of different scale features, CA-FPN realizes adaptive fusion of multiscale features to enhance the semantic information of the defect while avoiding background interference as much as possible. In particular, CA-FPN is very lightweight. Moreover, considering that defects are often of varying sizes and can be extremely tiny or slender, a flexible anchor-free detector CA-AutoAssign is proposed by combining CA-FPN and an anchor-free detection strategy AutoAssign. Based on the Alibaba Cloud Tianchi Fabric dataset and NEU-DET, CA-AutoAssign is compared with the state-of-the-art (SOTA) detectors. The experimental results show that CA-AutoAssign has the best detection performance with AP50 [mean average precision (mAP) with the intersection over union (IOU) threshold of 50%] reaching 89.1 and 82.7, respectively. Despite the improvement in accuracy, the processing time has barely increased. Furthermore, CA-FPN is applied to other classical detectors, and the experimental results demonstrate the competitiveness and generalization ability of CA-FPN. The code is available at https://github.com/EasonLuht/CA-AutoAssign.git.