Accurate detection of surface defects by decomposing unreliable tasks under boundary guidance

Danqing Kang, Jianhuang Lai, Yu Han

Abstract: Highlights•Unreliable annotations lead to poor defect detection, especially on boundaries.•Decomposing unreliable tasks on boundaries helps to learn robust features.•Simple but effective statistics recalibration module and adaptive summation module.•High focus on lightweight, introducing only negligible parameters and calculations.•Consistent performance improvements across different datasets and baselines.