Multi-Type Weld Defect Detection in Galvanized Sheet MIG Welding Using an Improved YOLOv10 Model | JoVE Visualize

Area of Science:

Computer Vision
Machine Learning
Industrial Automation
Quality Control

Background:

Automated weld inspection is challenging due to reflective surfaces, changing textures, and subtle defects in galvanized-sheet MIG welding.
Existing methods struggle with practical shop-floor constraints, such as limited computational resources (edge devices) and variable lighting conditions.
The need for robust, lightweight, and accurate weld inspection systems is critical for maintaining high-quality manufacturing.

Purpose of the Study:

To develop a compact and efficient deep learning model (YOLO-MIG) for accurate weld-seam inspection under realistic production conditions.
To address the limitations of current vision systems in detecting subtle defects on reflective galvanized steel.
To enable edge deployment of intelligent weld quality control systems.

Main Methods:

A novel compact object detection model, YOLO-MIG, was developed based on the YOLOv10n architecture.
Key modifications include a C2f-EMSCP backbone for preserving weak defect cues, a BiFPN neck for small-target feature fusion, and a C2fCIB head to reduce false positives from seam edges and illumination.
The model was trained and evaluated on a custom dataset of 2608 augmented images collected from a workshop environment.

Main Results:

YOLO-MIG achieved high performance with 98.4% mAP@0.5 and 56.29% mAP@0.5:0.95 on the test set.
The model is lightweight, featuring 1.83 million parameters and 3.87 MB FP16 weights, suitable for edge deployment.
Compared to the baseline YOLOv10n, YOLO-MIG significantly improved accuracy while reducing model size, parameters, and computational load.

Conclusions:

YOLO-MIG demonstrates superior accuracy and efficiency for weld-seam inspection in challenging industrial environments.
The proposed architectural modifications effectively handle reflective surfaces, varying textures, and subtle defects.
The model's lightweight nature makes it a practical solution for real-time, edge-based intelligent weld quality control.