Vision-Based Maritime Object Detection Covering Far and Tiny Obstacles
Ryota Yoneyama, Yuichiro Dake · IFAC-PapersOnLine · 2022
Though general object detection with deep learning has achieved great success in the maritime domain, recognizing far-away and tiny objects is still challenging. However, an autonomous vessel must be aware of objects even several nautical miles away for early planning. In this paper, we propose a far-away and tiny object detection framework with deep learning by utilizing a high-resolution image. The framework is two-fold: Small Object Detector (SOD) and Large Object Detector (LOD). SOD investigates a region around the horizon with a fine-resolution image aiming to recognize far-away and small objects. LOD targets large objects that are identifiable in an even coarse image. We evaluate our approach on datasets including objects of various sizes inshore or offshore. The results verify that our proposed scheme is effective against even far-away and tiny objects while keeping computational costs low.