AdaCon: Adaptive Context-Aware Object Detection for Resource-Constrained Embedded Devices
This work addresses efficiency challenges for deploying object detection on embedded devices, representing an incremental improvement.
The paper tackles the problem of high computational and energy requirements of object detection models on resource-constrained edge devices by leveraging prior knowledge about object co-occurrence probabilities to design an adaptive network, resulting in up to 45% reduction in energy consumption and 27% reduction in latency with a small loss in average precision.
Convolutional Neural Networks achieve state-of-the-art accuracy in object detection tasks. However, they have large computational and energy requirements that challenge their deployment on resource-constrained edge devices. Object detection takes an image as an input, and identifies the existing object classes as well as their locations in the image. In this paper, we leverage the prior knowledge about the probabilities that different object categories can occur jointly to increase the efficiency of object detection models. In particular, our technique clusters the object categories based on their spatial co-occurrence probability. We use those clusters to design an adaptive network. During runtime, a branch controller decides which part(s) of the network to execute based on the spatial context of the input frame. Our experiments using COCO dataset show that our adaptive object detection model achieves up to 45% reduction in the energy consumption, and up to 27% reduction in the latency, with a small loss in the average precision (AP) of object detection.