SmallNet: A Small Defects Detection Network for Magnetic Chips Based on Context-Weighted Aggregation and Feature Multiscale Loop Fusion

Accurate detection of surface defects on magnetic chips is a necessary and difficult task, especially for small defects, which lack discriminative and robust features due to their small size and weak characteristics. At present, the low accuracy of small defect detection seriously restricts the development of automated visual inspection and needs to be solved urgently. Thus, we propose a novel small defects detection network based on context-weighted aggregation and feature multiscale loop fusion. First, a context-weighted aggregation module (CAM) that enriches feature representations by combining context and attention mechanisms is proposed. We believe that any further improvements will be futile if robust feature representations cannot be obtained. Second, inspired by the mechanism of looking and thinking twice, we aggregate left-right feedback connections into feature pyramids and creatively propose a loop-shaped feature pyramid network (Loop-FPN), enabling multiscale features to be fused up and down and connected left and right. This loop-shaped structure makes the connection of each layer more direct and allows the features at each scale to be fully integrated, which improves the utilization of multiscale features and facilitates the detection of small defects. Finally, we apply the proposed network to practical detection and the results show that our network achieves 97.57% precision, 91.91% recall, and 98.39% AP, which are 0.13%, 1.43%, and 0.99% higher than the current best-performing comparative methods, respectively. Note to Practitioners—Current vision inspection technology has low accuracy and poor stability in small defect detection, which cannot meet the industrial inspection requirements. Small defect detection has become the biggest challenge in the field of visual inspection and has seriously restricted its development. Our proposed network can solve this problem well by mining small defect context, fusing multiscale features and utilizing attention mechanisms, and has been successfully applied in magnetic chip production line. Furthermore, we developed an image acquisition system that can capture defects on all surfaces with high accuracy and without dead space, allowing our network to not only detect small defects on magnetic chips of varying sizes and irregular shapes, but also to adapt to changes in lighting conditions, backgrounds, and viewpoints. Our work provides an effective, reliable, and convenient quality control solution for magnetic chip production.