A lightweight YOLOv11-based framework for small steel defect detection with a newly enhanced feature fusion module

Le résumé fourni par la source

In order to address the challenges of deployment difficulties and low small-object detection efficiency in current deep learning-based defect detection models on terminal devices with limited computational capacity, this paper proposes a lightweight steel surface defect detection model, Pyramid-based Small-target Fusion YOLO (PSF-YOLO), based on an improved YOLOv11n object detection framework. The model employs a low-parameter Ghost convolution (GhostConv) to substantially reduce the required computational resources. Additionally, the traditional feature pyramid network structure is replaced with a Multi-Dimensional-Fusion neck (MDF-Neck) to enhance small-object perception and reduce the number of model parameters. Moreover, to achieve multi-dimensional integration in the neck, a Virtual Fusion Head is utilized, and the design of an Attention Concat module further improves target feature extraction, thereby significantly enhancing overall detection performance. Experimental results on the GC10-DET+ dataset demonstrate that PSF-YOLO reduces model parameters by 25% while achieving improvements of 3.2% and 3.3% in [Formula: see text] and [Formula: see text], respectively, compared to the baseline model. This approach offers valuable insights and practical applicability for deploying defect detection models on terminal devices with limited computational resources.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.