Improving Underwater Object Detection and Classification using Deep Learning for ROVs - kollam, Kalpana; Ashwini, K (Journal of Neonatal Surgery, 2025)
The identification and categorization of things, particularly metallic artefacts, is a substantial problem in underwater research for many reasons. This paper presents a thorough algorithmic framework for underwater metal object detection and classification using remotely operated vehicles (ROVs) and computer vision. The experimental design section describes the steps used to detect objects underwater with ROVs. The algorithm is subjected to several processes, including picture enhancement, object identification using YOLOv3, and object classification using Deep learning algorithm. Both the training and testing datasets provide a wide range of underwater images with different lighting, object sizes, and complexity of backgrounds. Analyses and Results detail the assessment of the combined algorithm's performance. We use the industry-standard metrics for object detection, such as F1 score, precision, recall, and Intersection over Union (IoU). When tested on a variety of metallic items, the programme consistently returns positive results. Further validation of the algorithm's ability in identifying and classifying specific items underwater is provided by a comparative examination of precision, recall, and F1 score across different classes.