Visual Marine Debris Detection using Yolov5s for Autonomous Underwater Vehicle
Cheng Siong Chin, Aloysius Bo Hui Neo, Simon Chong Wee See · 2022
The trash in the ocean is causing harm to the marine environment. The current most used removal technique is the use of trawlers. It is a highly laborious job and requires high costs as well. With the help of Autonomous Underwater Vehicles (AUVs), removing marine debris could be one of the best and cheapest solutions available. This paper evaluates the use of the You Only Live Once Version 5 Small (YOLOv5s) to compare with the other networks used to identify marine debris. Without fine-tuning the YOLOv5s model in this study, it can achieve a Mean Average Precision (mAP) of 0.681 and an inference speed of 153.9 frames per second. It shows an improvement in mAP compared to YOLOv2, Tiny-YOLO, and Single Shot Multibox Detector.