An Efficient Anchor-Free Defect Detector With Dynamic Receptive Field and Task Alignment

Defect detection aims to locate and classify defects in images, which is a necessary yet challenging task in industrial product quality monitoring. The current anchor-based detectors have weak generalization performance due to their inability to consider numerous scale priors. Moreover, the basic networks lack the ability to dynamically capture and utilize multiscale feature representations, resulting in low accuracy in industrial defect detection. To counter these challenges, an efficient anchor-free detector with dynamic receptive field assignment (DRFA) and task alignment is proposed. First, a feature pyramid structure with DRFA is innovatively designed to sufficiently extract multiscale feature representation and flexibly adjust the receptive field to detect diverse defects. Second, a task decoupling prediction mechanism is proposed to improve localization and classification prediction capabilities by introducing feature reassembly and task-specific information enhancers. Next, an anchor-free-based deep supervision with task-aligned is presented to encourage both to make accurate and consistent predictions, thereby effectively improving the overall detection performance. Finally, three industrial defect datasets (NEU-DET, PCB, WELD) are employed for experiments. The results show that the proposed method achieves 5.3% higher average AP than other state-of-the-art detectors.