TL;DR
A dynamic gated adaptive fusion network (DGAF-Net) built upon the YOLO11n baseline for real-time steel surface defect detection, validating its feasibility for real-time defect detection in resource-constrained industrial environments.
Steel surface defect detection is vital for guaranteeing product quality in contemporary manufacturing. However, traditional steel surface defect detection algorithms often face challenges due to insufficient resilience in feature extraction under complex backgrounds. To address this, we present a framework for def...
Steel surface defect detection is a critical component of intelligent manufacturing and industrial visual quality control. However, due to large variations in defect scale, elongated morphologies, strong directional continuity, and high inter-class visual similarity, existing detection methods struggle to achieve a des...
Strip steel surface defect detection is a critical task in industrial quality control, yet it remains persistently challenging owing to the low contrast between defects and the rolling texture, the sparse and uneven defect distribution, and the highly variable defect morphology. Existing detectors tend to operate in th...
To address the challenges of low localization accuracy for insulator defects in complex inspection scenarios and excessive model redundancy that hinders edge deployment, this paper proposes FS-YOLO, a lightweight defect detection algorithm. First, a C3k2 ConvFormer Gated Linear Unit (C3k2-CFGLU) module replaces the ori...
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