Confidence–gradient reweighting and lightweight feature enhancement algorithm for steel surface defect detection

TL;DR

This paper introduces GRACE, an algorithm that improves steel surface defect detection by addressing small target sizes and class imbalance.

Contribution

GRACE combines dynamic reweighting and lightweight feature enhancement for better defect detection in steel surfaces.

Findings

GRACE improves mAP@0.5:0.95 by 1.00 percentage points and mAP@0.5 by 1.19 percentage points over YOLO11s on the NEU-DET dataset.

The algorithm maintains real-time performance with 9.56 M parameters and shows robustness on complex textured backgrounds.

GRACE achieves competitive results on the GC10-DET and X-SDD datasets, confirming its adaptability to different defect distributions.

Abstract

Steel surface defect detection is susceptible to small target sizes, low contrast, and class imbalance. To this end, we propose the Gradient-Reweighting with Awareness of Confidence and Lightweight Feature Enhancement (GRACE) algorithm built upon YOLO11s, composed of two synergistic modules: Dynamic Sampling with Confidence-Gradient Balanced Sampling Mechanism (DS-CBSM++) performs dynamic reweighting via joint confidence-gradient feedback, improving the separability of hard examples and long-tailed classes; Lightweight Feature Enhancement Network (Lite-FEN) introduces lightweight channel/spatial enhancement at the P3 layer to strengthen shallow textures and boundary cues while keeping computation low. Experiments on the NEU-DET dataset show that the baseline YOLO11s achieves an mAP@0.5:0.95 of 42.66% and an mAP@0.5 of 74.69%. GRACE achieves 43.66% and 75.88%, respectively, improving over the baseline by 1.00 percentage points and 1.19 percentage points, with 9.56 M parameters, suitable for real-time inference. These results indicate that GRACE yields more robust detection and localization of small defects under complex textured backgrounds.Additional experiments on the GC10-DET and X-SDD datasets further confirm that GRACE maintains competitive performance across different steel surface defect distributions.

Figures