An Underwater Crack Detection System Combining New Underwater Image-Processing Technology and an Improved YOLOv9 Network

Autor: Xinbo Huang, Chenxi Liang, Xinyu Li, Fei Kang
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Sensors, Vol 24, Iss 18, p 5981 (2024)
Druh dokumentu: article
ISSN: 24185981
1424-8220
DOI: 10.3390/s24185981
Popis: Underwater cracks are difficult to detect and observe, posing a major challenge to crack detection. Currently, deep learning-based underwater crack detection methods rely heavily on a large number of crack images that are difficult to collect due to their complex and hazardous underwater environments. This study proposes a new underwater image-processing method that combines a novel white balance method and bilateral filtering denoising method to transform underwater crack images into high-quality above-water images with original crack features. Crack detection is then performed based on an improved YOLOv9-OREPA model. Through experiments, it is found that the new image-processing method proposed in this study significantly improves the evaluation indicators of new images, compared with other methods. The improved YOLOv9-OREPA also exhibits a significantly improved performance. The experimental results demonstrate that the method proposed in this study is a new approach suitable for detecting underwater cracks in dams and achieves the goal of transforming underwater images into above-water images.
Databáze: Directory of Open Access Journals
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje