TL;DR
An improved lightweight detection model (MBHS-DETR) is proposed that maintains high accuracy while also ensuring real-time performance, providing an effective and practical solution for bearing surface defect detection.
This study addresses the challenges of inefficient multi-scale feature fusion and high computational complexity in detecting micro defects on welding surfaces, which can compromise detection accuracy and real-time performance. An object detection framework is proposed that integrates a lightweight architecture with ad...
Abstract Surface defect detection is essential for maintaining quality in industrial products, but in real industrial applications it faces a dilemma among accuracy, processing speed, and model lightweight. To address this challenge, we propose a method named global attention mechanism for deeply separable YOLO (simply...
Bearing, as a critical component in industrial production, directly dictates equipment performance. Bearing surface defects are typically minute in scale, indistinct at their boundaries, and prone to interference from surface textures and light reflections, thereby posing substantial challenges to detection. This paper...
This network alleviates the trade-off between detection accuracy and computational efficiency, enabling efficient and robust detection of multi-scale defects on metal substrates and substantially reduces parameter redundancy while maintaining multi-scale feature representation capabilities.
To address the limitations of YOLOv8n in steel surface defect detection–namely, its large parameter count and computational cost, insufficient multi-scale feature modeling capability, and poor scale adaptability of the detection head– we propose a lightweight improved model named EDGI-YOLO. The model is restructured al...
Results demonstrate that GSLA-YOLOv11 provides an effective trade-off among detection accuracy, model compactness, and real-time deployment capability for steel surface defect inspection.
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