Efficient Corrosion Detection on Metal Surface Using Deep Learning Technique

Ky Phuc, Phan Nguyen (School of Industrial Engineering and Management, International University, Vietnam National University – Ho Chi Minh City, Vietnam.)
Tin, Tran Lam Trung
Luu, Trong Hieu (Can Tho University)

Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Vol 13, No 3: September 2025

This study examines how deep learning models can improve corrosion detection, comparing YOLOv7 with its more advanced version, YOLOv8. Both models were trained on a diverse set of images showing different types and levels of corrosion on metal surfaces. Their performance was assessed using standard industry metrics, including accuracy, F1-score, recall, and mean average precision (mAP). The results clearly show that YOLOv8 outperforms YOLOv7 in all areas. It achieves higher recall, precision, and F1-score, demonstrating its improved ability to detect and classify corroded areas. Notably, YOLOv8 is better at identifying small or early-stage corrosion, which is crucial for timely maintenance. Additionally, it processes images faster than YOLOv7, making it more suitable for real-time applications. This study also suggests integrating YOLOv8 with robotic arms equipped for laser cleaning, allowing for automated and precise corrosion removal. This system could improve maintenance efficiency, reduce costs, and enhance the safety and reliability of infrastructure.