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This is a In Vitro Study study.

August 20, 2025AlgorithmsOpen Access

Optimized Adaptive Multi-Scale Architecture for Surface Defect Recognition

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Proposed lightweight model using YOLOv11 improves surface defect recognition accuracy, suggesting efficiency gains.

The optimized adaptive multi-scale architecture improved mAP@0.5 to 0.758 on the NEU-DET dataset, enhancing defect detection.
Results indicate a 39.1% reduction in parameters and a 49.2% decrease in computational complexity compared to the baseline.
Implementation involved an improved YOLOv11 with a Reversible Connected Multi-Column Network and embedded FasterNet.
The lightweight model shows potential for real-time applications in industrial settings, optimizing both accuracy and efficiency.

Chang et al. (2025) studied this question.

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