2024/03/20 by Wenjun Huang, Hanning Chen, Huang, Wenjun +13
Engineering · Environmental Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Maritime Navigation and Safety #Maritime Transport Emissions and Efficiency
paper · pdf · doi:10.48550/arxiv.2403.14027
openalex publication_date 2024/03/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Detecting marine objects inshore presents challenges owing to algorithmic intricacies and complexities in system deployment. We propose a difficulty-aware edge-cloud collaborative sensing system that splits the task into object localization and fine-grained classification. Objects are classified either at the edge or within the cloud, based on their estimated difficulty. The framework comprises a low-power device-tailored front-end model for object localization, classification, and difficulty estimation, along with a transformer-graph convolutional network-based back-end model for fine-grained classification. Our system demonstrates superior performance ([email protected] +4.3%) on widely used marine object detection datasets, significantly reducing both data transmission volume (by 95.43%) and energy consumption (by 72.7%) at the system level. We validate the proposed system across various embedded system platforms and in real-world scenarios involving drone deployment.