EcoSense: Energy-Efficient Intelligent Sensing for In-Shore Ship Detection through Edge-Cloud Collaboration
CoRR(2024)
Abstract
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
system level. We validate the proposed system across various embedded system
platforms and in real-world scenarios involving drone deployment.
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