IAMF-YOLO: Metal Surface Defect Detection Based on Improved YOLOv8

Abstract

For cylinder production, visual inspection of surface defects on cylinder joints holds great significance, yet the task is challenged by the high reflectivity on joint surfaces and the presence of numerous small defects. To address this issue, a metal surface defect detection network, named information augmentation and multiscale feature fusion with YOLO (IAMF-YOLO) based on YOLOv8, is proposed in this article, aiming to enhance the accuracy of joint surface defect detection. The IAMF-YOLO detection network incorporates an information augmentation strategy into the backbone network and introduces a novel backbone network called the information augmentation network (IAN). This IAN reduces the information loss during feature downsampling extraction. Furthermore, to boost the detection performance of small defects on joint surfaces, improvements have been made to the path aggregation network (PAN), and a parallel multiscale feature pyramid network (PMFPN) is introduced. PMFPN is designed to separate feature fusion into two stages: an initial feature fusion stage and an incremental feature fusion stage, of which the latter further merges the features of the former. Through ablation studies and comparative experiments on IAMF-YOLO, it is demonstrated that this model surpasses YOLOv8 in terms of detection accuracy and precision. Specifically, it achieves an accuracy of 88.6% on a self-constructed dataset, 91.0% on the NEU-DET dataset, and 84.2% on the GC10-DET dataset. Furthermore, this study demonstrates the robustness of the proposed model against environmental disturbances through actual defect detection under varying lighting conditions.

Topics & Concepts