You Only Look Once–Aluminum: A Detection Model for Complex Aluminum Surface Defects Based on Improved YOLOv8

Abstract

Detecting aluminum defects in industrial environments presents significant challenges related to low-resolution images, subtle damage features, and an imbalance between easy and difficult samples. The You Only Look Once–Aluminum (YOLO-AL) algorithm proposed in this paper addresses these challenges. Firstly, to enhance the model’s performance on low-resolution images and small object detection, as well as to improve its flexibility and adaptability, C2f-US replaces the first two CSP bottleneck with 2 Convolutions (C2f) layers in the original Backbone network. Secondly, to boost multi-scale context capture and strip defect detection, a CPMSCA mechanism with a class-symmetric structure is proposed and integrated at the end of the Backbone network. Thirdly, to efficiently capture both high-level semantics and low-level spatial details, and improve detection of complex aluminum surface defects, ODE-RepGFPN is introduced to replace the entire Neck network. Finally, to address the imbalance between hard and easy samples, Focaler-WIoU is proposed. Extensive experiments conducted on the publicly available AliCloud dataset (APDDD) demonstrate that YOLO-AL achieves 86.5%, 77.8%, and 81.5% for Precision, Recall, and mAP@0.5, respectively, surpassing both the baseline model and other state-of-the-art methods. The model can be integrated with an industrial camera system for the automated inspection of aluminum profiles in a production environment.