A Precise Semantic Segmentation Model for Seabed Sediment Detection Using YOLO-C

Abstract

Semantic segmentation methods have been successfully applied in seabed sediment detection. However, fast models like YOLO only produce rough segmentation boundaries (rectangles), while precise models like U-Net require too much time. In order to achieve fast and precise semantic segmentation results, this paper introduces a novel model called YOLO-C. It utilizes the full-resolution classification features of the semantic segmentation algorithm to generate more accurate regions of interest, enabling rapid separation of potential targets and achieving region-based partitioning and precise object boundaries. YOLO-C surpasses existing methods in terms of accuracy and detection scope. Compared to U-Net, it achieves an impressive 15.17% improvement in mean pixel accuracy (mPA). With a processing speed of 98 frames per second, YOLO-C meets the requirements of real-time detection and provides accurate size estimation through segmentation. Furthermore, it achieves a mean average precision (mAP) of 58.94% and a mean intersection over union (mIoU) of 70.36%, outperforming industry-standard algorithms such as YOLOX. Because of the good performance in both rapid processing and high precision, YOLO-C can be effectively utilized in real-time seabed exploration tasks.