A Deformable Attention-Based Detection Transformer with Cross-Scale Feature Fusion for Industrial Coil Spring Inspection
Matteo Rossi, Pony Matt

TL;DR
This paper introduces MSD-DETR, a deformable attention-based detection transformer with cross-scale feature fusion, achieving high accuracy and real-time performance for industrial coil spring defect detection amidst complex backgrounds and scale variations.
Contribution
The paper presents a novel detection framework combining structural re-parameterization, deformable attention, and cross-scale feature fusion for improved industrial defect detection.
Findings
Achieves 92.4% [email protected] at 98 FPS on real-world dataset.
Outperforms YOLOv8 and baseline RT-DETR in accuracy while maintaining real-time speed.
Establishes a new benchmark for coil spring defect inspection.
Abstract
Automated visual inspection of locomotive coil springs presents significant challenges due to the morphological diversity of surface defects, substantial scale variations, and complex industrial backgrounds. This paper proposes MSD-DETR (Multi-Scale Deformable Detection Transformer), a novel detection framework that addresses these challenges through three key innovations: (1) a structural re-parameterization strategy that decouples training-time multi-branch topology from inference-time efficiency, enhancing feature extraction while maintaining real-time performance; (2) a deformable attention mechanism that enables content-adaptive spatial sampling, allowing dynamic focus on defect-relevant regions regardless of morphological irregularity; and (3) a cross-scale feature fusion architecture incorporating GSConv modules and VoVGSCSP blocks for effective multi-resolution information…
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Taxonomy
TopicsAdvanced Neural Network Applications · Industrial Vision Systems and Defect Detection · Infrastructure Maintenance and Monitoring
