Edge-Cloud Collaborated Object Detection via Difficult-Case Discriminator
This addresses the problem of high communication costs in edge-cloud collaboration for object detection, offering a practical solution for applications on edge devices, though it is incremental in improving existing methods.
The paper tackles the challenge of deploying heavyweight object detection algorithms on resource-constrained edge devices by proposing a small-big model framework with a difficult-case discriminator, achieving 94.01%-97.84% object detection with only about 50% images uploaded to the cloud and 91.22%-92.52% mAP compared to full cloud upload.
As one of the basic tasks of computer vision, object detection has been widely used in many intelligent applications. However, object detection algorithms are usually heavyweight in computation, hindering their implementations on resource-constrained edge devices. Current edge-cloud collaboration methods, such as CNN partition over Edge-cloud devices, are not suitable for object detection since the huge data size of the intermediate results will introduce extravagant communication costs. To address this challenge, we propose a small-big model framework that deploys a big model in the cloud and a small model on the edge devices. Upon receiving data, the edge device operates a difficult-case discriminator to classify the images into easy cases and difficult cases according to the specific semantics of the images. The easy cases will be processed locally at the edge, and the difficult cases will be uploaded to the cloud. Experimental results on the VOC, COCO, HELMET datasets using two different object detection algorithms demonstrate that the small-big model system can detect 94.01%-97.84% of objects with only about 50% images uploaded to the cloud when using SSD. In addition, the small-big model averagely reaches 91.22%- 92.52% end-to-end mAP of the scheme that uploading all images to the cloud.