Detecting Maritime Obstacles Using Camera Images

Abstract

Aqua farms will be the most frequently encountered obstacle when autonomous ships sail along the coastal area of Korea. We used YOLOv5 to create a model that detects aquaculture buoys. The distances between the buoys and the camera were calculated based on monocular and stereo vision using the detected image coordinates and compared with those from a laser distance sensor and radar. A dataset containing 2700 images of aquaculture buoys was divided between training and testing data in the ratio of 8:2. The trained model had precision, recall, and mAP of 0.936%, 0.903%, and 94.3%, respectively. Monocular vision calculates the distance based on camera position estimation and water surface coordinates of maritime objects, while stereo vision calculates the distance by finding corresponding points using SSD, NCC, and ORB and then calculating the disparity. The stereo vision had small error rates of −3.16% and −14.81% for short (NCC) and long distances (ORB); however, large errors were detected for objects located at a far distance. Monocular vision had error rates of 2.86% and −4.00% for short and long distances, respectively. Monocular vision is more effective than stereo vision for detecting maritime obstacles and can be employed as auxiliary sailing equipment along with radar.

Keywords

• autonomous ship
• object detection
• YOLOv5
• monocular vision
• stereo vision