Edge-AI for Buoy Detection and Mussel Farming: A Comparative Study of YOLO Frameworks

Despite advancements in computer vision technologies, maritime environments continue to pose significant challenges. Varying weather conditions, dynamic water surfaces, and the presence of both large and small objects hamper object detection and tracking and, more in general, the development of robust AI solutions for maritime industry. Addressing this concern, we propose a lightweight deep learning approach for robust environment monitoring and, in particular, tailored for the detection of buoys as those used for offshore submerged mussel farming long-lines. Such industrial applications are still challenging as, due to unstable internet connectivity, autonomous and efficient object detection cannot rely on external resources. Our model, built and benchmarked upon several You Only Look Once (YOLO) frameworks coupled with horizon line segmentation, leverages both custom and open access data and is tested for deployment on edge device for practical demonstration. The proposed method uses Deep Hough Transform to determine the maritime horizon line and exclude far-off objects/land, enhancing the system’s robustness to false positives. YOLOv3, YOLOv4, YOLOv5, and YOLOv8 and their variants, were tested and evaluated based on several performance and efficiency metrics. Preliminary findings indicate that YOLOv8-Nano was particularly effective, demonstrating high computational efficiency (7.2 GFLOPs) and real-time inference at 24.1 fps on an NVIDIA Jetson Nano, with a mAP of 69.10 and achieving an optimal trade-off between efficiency and accuracy. Such enhanced object detection capabilities could substantially benefit the maritime industry, significantly improving operational safety and reducing the risk of economic losses and environmental damage.