Lightweight EDGI-YOLO improves steel surface defect detection in image-based benchmark study

A computer-vision study reports that EDGI-YOLO, a lightweight detector designed for steel surface defects, achieved 78.85% mAP at an intersection-over-union threshold of 0.5 on the NEU-DET benchmark, 3.94 percentage points above the YOLOv8n baseline. The model used 1.66 million parameters and 6.0 GFLOPs, according to the research by Haodong Wu and Fengyun Cao. The results provide benchmark evidence for image-based defect detection, rather than validation on a production steel line.

The work focused on six classes of hot-rolled steel surface defects represented in NEU-DET: crazing, inclusions, patches, pitted surfaces, rolled-in scale and scratches. The dataset contains 1,800 grayscale images at 200×200 pixels, with 300 images per class. Researchers randomly divided the data into 1,440 training, 180 validation and 180 test images, an 8:1:1 split. Images were scaled to 640×640 pixels during training, and all reproduced models used the same data split, training duration and augmentation settings.

EDGI-YOLO was developed from YOLOv8n, which had approximately 3.01 million parameters and 8.2 GFLOPs. Its architecture combines four modules. ECA-GC first screens feature channels with efficient channel attention and then applies global context modulation. DSEConv separates spatial filtering from channel transformation with depthwise separable convolution and adds Squeeze-and-Excitation attention. AllGhostC2f replaces standard C2f blocks with a Ghost-convolution-based structure designed to reduce redundant feature generation. Finally, IAFHead uses DSEConv in its classification and regression branches, fuses information across three feature scales with CrossScaleSE attention and applies different branch depths to shallow and deep features.

The ablation experiments examined every single-module, two-module and three-module combination as well as the complete model. The full four-module configuration reached 78.85% mAP@0.5, compared with 74.91% for the baseline. Combining DSEConv and AllGhostC2f without the other components produced 74.73%, while ECA-GC paired with IAFHead reached 78.03%. The full model’s gain over the baseline was not reproduced by either component alone, indicating that the measured performance depended on the combined design.

Removing one component from the complete system also reduced performance. The configuration without IAFHead achieved 76.97% mAP@0.5, while models without AllGhostC2f, DSEConv or ECA-GC achieved 76.02%, 76.69% and 76.03%, respectively. These reductions ranged from 1.88 to 2.83 percentage points relative to the full model. Separate tests of ECA-GC found that its cascaded form at backbone layer 5 produced 76.42% mAP@0.5, exceeding the standalone ECA, standalone GC, reversed-cascade and parallel arrangements by 0.80, 0.36, 0.37 and 1.34 percentage points, respectively.

In comparisons with other detectors, EDGI-YOLO recorded the highest overall mAP@0.5 and mAP from 0.5 to 0.95 among the models evaluated, with scores of 78.85% and 44.70%. Its precision was 79.52%, while recall was 71.54%. Class-level AP@0.5 included 58.05% for crazing, 90.36% for patches, 67.81% for rolled-in scale and 97.06% for scratches. Compared with YOLOv8n, crazing increased from 46.5% to 58.05%, and rolled-in scale from 51.9% to 67.81%.

Results from five training seeds gave mean mAP@0.5 values of 77.98% ± 0.92% for EDGI-YOLO and 75.12% ± 1.00% for YOLOv8n. EDGI-YOLO produced a higher score with each of the five seeds, with improvements ranging from 0.30 to 4.46 percentage points. The study therefore reported that its advantage over the baseline was not confined to one initialization, although the article does not provide a separate statistical hypothesis test.

Deployment was evaluated on a system with an 11th-generation Intel Core i5-11400H processor and an NVIDIA GeForce RTX 3050 Laptop GPU with 4 GB of memory. With TensorRT FP16, EDGI-YOLO processed 159.26 frames per second at 6.28 milliseconds per image, compared with 146.68 frames per second for YOLOv8n, and retained a reported 4.27-percentage-point mAP@0.5 advantage. In the ONNX CPU configuration, it ran at 27.16 frames per second with 36.82 milliseconds per image, versus 24.87 frames per second for YOLOv8n. Reported file sizes were smaller in PyTorch and ONNX, but the study did not separately measure loading time or memory use.

Cross-dataset testing used 3,570 aluminum surface images in GC10-DET, covering 10 defect categories and an 8:1:1 split. EDGI-YOLO achieved 64.29% mAP@0.5, 4.38 percentage points above YOLOv8n, while retaining 1.66 million parameters and 6.05 GFLOPs. The authors reported improvements of 2.44 percentage points for crease, 1.16 for inclusion and 3.90 for rolled pit. The evidence remains limited to two public image datasets, selected comparison models and the specified inference hardware; it does not establish performance on every industrial surface, production environment or defect type.

Subject of Research: Lightweight neural-network detection of steel and aluminum surface defects in image-based benchmarks

Article Title: A lightweight model for steel surface defect detection based on EDGI-YOLO

Article References: Wu, H., & Cao, F. (2026). A lightweight model for steel surface defect detection based on EDGI-YOLO. Discover Artificial Intelligence, 6(1), Article 1280. https://doi.org/10.1007/s44163-026-02329-2

Image Credits: AI Generated

DOI: 10.1007/s44163-026-02329-2

Keywords: EDGI-YOLO, steel surface defects, YOLOv8n, computer vision, object detection, NEU-DET, GC10-DET, edge deployment, TensorRT, lightweight, model, steel