An Improved Multiscale Semantic Enhancement Network for Aluminum Defect Detection

Abstract

Defect detection in aluminum profiles helps to ensure product quality. However, aluminum defects suffer from variable object scales and high defect-background similarity issues. To address these issues, an improved multiscale semantic enhancement YOLOv5 defect detection method, namely CW-YOLOv5, has been proposed. First, to reduce missed defections, a cross layer link network (CLLN) is introduced into YOLOv5 to capture complete and comprehensive features during the feature extraction process. To accurately detect small-size defects, the weighted feature fusion mechanism (WFFM) is proposed to be added to shallow and high-level feature fusion networks. Finally, the soft non-maximum suppression (Soft-NMS) module is introduced into YOLOv5 to tackle the feature candidate box filtering task to reduce false and missed detections. The proposed defect detection network is applied to the public Tianchi aluminum profile defect dataset (TCAPD), and CW-YOLOv5 gain the improvement of [email protected] by 2.3% compared to the baseline network. Moreover, the experimental results demonstrate that CW-YOLOv5 can effectively detect aluminum surface defects.

Keywords

• Aluminum defect detection
• YOLOv5
• multiscale semantic enhancement network
• feature fusion mechanism