IoU-Aware Clustering for Anchor Configuration Determination in Efficient Defect Detection

Abstract

Deep-learning-based object detection has gained widespread application in surface defect inspection, with anchor-based detectors achieving remarkable success by utilizing dense anchors to align with defects. Determining the optimal anchor configuration, i.e., sizes and aspect ratios of anchor boxes, remains a critical challenge, particularly when addressing defects with significant shape variations. While previous studies have predominantly focused on developing more efficient network architectures and learning strategies, the problem of anchor configuration determination has not been thoroughly explored. To address this gap, this paper proposes the IoU-Aware Clustering (IAC) algorithm, which autonomously learns suitable anchor configurations by extracting shape priors from diverse defects. IAC takes the training bounding boxes as potential clustering centers and selects a subset that aligns with the shape distribution of the training samples. The algorithm involves only a single hyper-parameter, the anchor number k, making it highly adaptable to various scenarios. Experimental results demonstrate that IAC can effectively generate anchor configurations tailored to defect shapes, significantly improving the mean Average Precision (mAP) by 6.9% and 14.4% on two industrial defect datasets with substantial shape variations.