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.

提出了一种难度感知的边缘 - 云协同感知系统，用于检测岸边的海洋物体，该系统将任务分为物体定位和细粒度分类，并根据预估的难度将物体进行边缘或云内分类。该系统在广泛使用的海洋物体检测数据集上表现出卓越性能（mAP@0.5 +4.3%），在系统级别显著降低了数据传输量（95.43%）和能源消耗（72.7%），并在涉及无人机部署的真实场景和各种嵌入式系统平台上验证了提出的系统。