MA-YOLO: A Method for Detecting Surface Defects of Aluminum Profiles with Attention Guidance

Abstract

Aluminum Profiles (APs) are aluminum materials obtained by hot melting and extruding aluminum rods. It has the characteristics of low cost, strong plasticity, easy processing, and recyclability, and therefore plays an important role in industrial production. However, defects such as Non-Conductive (NC), Scratch, Orange Peel (OP), and Dirty Point (DP) often occur during the production and processing of APs, which can seriously affect the quality of APs. In addition, surface defects of APs also have problems such as fuzzy regional definition, large-scale variation, imbalance of aspect ratio, and high inter-class defect similarity, making defect detection more challenging. To solve these problems, this paper proposes an attention-guided object detection algorithm called MA-YOLO, specifically for Surface Defect Detection (SDD) of APs. The algorithm is based on YOLOv5s. Firstly, the K-Means++ clustering algorithm is used to optimize the anchor boxes, which alleviates the problem of aspect ratio imbalance. Secondly, by improving the multi-scale Feature Fusion Network (FFN), the detection performance of the model to detect the defects with unbalanced aspect ratio is improved, and the adaptability of the model to defects of different scales is enhanced. Finally, a novel Max Pooling Average Pooling (MA) attention module is proposed to improve the overall detection performance of the model, especially for small-scale defects. Experimental results on the aluminum profile surface defect dataset show that MA-YOLO has better detection performance and superiority than the current mainstream object detection algorithms, and compared with the baseline YOLOv5s, the mAP50 and F1 score are increased by 2.9% and 2.2%, respectively, while keeping the model lightweight. This indicates that MA-YOLO has broad application prospects in the surface defect detection of APs.