EcoSense: Energy-Efficient Intelligent Sensing for In-Shore Ship Detection through Edge-Cloud Collaboration
<|reference_start|>EcoSense: Energy-Efficient Intelligent Sensing for In-Shore Ship Detection through Edge-Cloud Collaboration: 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 (mAP@0.5 +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.<|reference_end|>
@article{huang2024ecosense:,
    title={EcoSense: Energy-Efficient Intelligent Sensing for In-Shore Ship
  Detection through Edge-Cloud Collaboration},
    author={Wenjun Huang, Hanning Chen, Yang Ni, Arghavan Rezvani, Sanggeon Yun,
  Sungheon Jeon, Eric Pedley, Mohsen Imani},
    journal={arXiv preprint arXiv:2403.14027},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.14027},
    primaryClass={cs.CV}
}