SAMF-YOLO: A self-supervised, high-precision approach for defect detection in complex industrial environments

Jun Huang, Shamsul Arrieya Ariffin, Qiang Zhu, Wanting Xu and Qun Yang

PLOS ONE, 2025, vol. 20, issue 7, 1-21

Abstract: As object detection models grow in complexity, balancing computational efficiency and feature expressiveness becomes a critical challenge. To address this, we propose SAMF-YOLO, a novel model integrating three key components: SONet, BFAM, and FASFF-Head. The UniRepLKNet backbone, enhanced by the Star Operation, expands the feature space with high efficiency. FASFF-Head performs adaptive multi-scale feature fusion with minimal overhead, and the Bi-temporal Feature Aggregation Module (BFAM) strengthens the detection of small defects. Additionally, the Focaler-IoU loss improves bounding box regression for challenging object scales, and a self-supervised contrastive learning strategy enhances feature representation and model robustness without relying on labeled data. Experimental results demonstrate that SAMF-YOLO surpasses YOLOv11s with a 6.38% improvement in mAP@0.5 and a notable reduction in computational cost, confirming its superiority in accuracy, efficiency, and robustness. The code is released at https://github.com/Missing24ff/SAMF-YOLO.git.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0327001

DOI: 10.1371/journal.pone.0327001

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