Latest revision as of 12:14, 20 July 2026

Abstract

Traditional methods for detecting surface defects in steel typically rely on manual visual inspection, eddy current testing, magnetic particle inspection, and machine vision. These methods often struggle to adapt to defects of varying scales and complex shapes, resulting in insufficient feature extraction, false detections, missed detections, and low detection accuracy. To address these issues, we propose an enhanced YOLOv8n model named MAM-YOLO, which aims to optimize defect detection on steel surfaces through multi-scale feature enhancement. By redesigning the convolution modules, the model improves accuracy in identifying surface defects, crazing, scratches, and patches. Furthermore, the design of a new module enhances processing capabilities for diverse surface features. A new loss function optimization is also applied to better identify crack characteristics. Experimental results on the NEU-DET dataset demonstrate that MAM-YOLO increases mAP by 5.4% and improves accuracy by 5.9% compared to the original YOLOv8n. Additionally, the model’s generalization capability was verified on the KolektorSDD dataset, achieving performance superior to the YOLOv8n baseline. Therefore, the proposed MAM-YOLO model offers significant advantages in precision and robust feature extraction for identifying steel surface defects.OPEN ACCESS Received: 25/11/2025 Accepted: 27/01/2026 Published: 21/07/2026

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