Evaluating YOLOV5, YOLOV6, YOLOV7, and YOLOV8 in Underwater Environment: Is There Real Improvement?
Boris Gašparović, Goran Mauša, Josip Rukavina, Jonatan Lerga
Abstract
This paper compares several new implementations of the YOLO (You Only Look Once) object detection algorithms in harsh underwater environments. Using a dataset collected by a remotely operated vehicle (ROV), we evaluated the performance of YOLOv5, YOLOv6, YOLOv7, and YOLOv8 in detecting objects in challenging underwater conditions. We aimed to determine whether newer YOLO versions are superior to older ones and how much, in terms of object detection performance, for our underwater pipeline dataset. According to our findings, YOLOv5 achieved the highest mean Average Precision (mAP) score, followed by YOLOv7 and YOLOv6. When examining the precision-recall curves, YOLOv5 and YOLOv7 displayed the highest precision and recall values, respectively. Our comparison of the obtained results to those of our previous work using YOLOv4 demonstrates that each version of YOLO detectors provides significant improvement.