Development of a lightweight cross-scale decoupling feature fusion network for surface defect detection in permanent magnets

Abstract Permanent magnet motors may suffer from imperceptible localized demagnetization due to surface damage during operation. This paper proposes a lightweight cross-scale decoupling feature fusion network (LCDFFN) for permanent magnets defect detection, which introduces separable convolution in the backbone and incorporates the adaptive multi-scale feature fusion with interactive attention mechanism to enhance small target detection. The adaptive attention module (AAM) and feature enhancement module based on dilated convolution are introduced in the feature fusion network, improving multi-scale object detection. Finally, the decoupled feature prediction network outputs the defect identification feature map. In addition, we also propose a novel fuzzy intersection over the union loss function. LCDFFN achieves a [email protected] of 97.8%, with 108.4M parameters and a detection speed of 113.62 FPS. The proposed method significantly improves defect image detection for permanent magnets and is highly practical for industrial production.