Edge-Cloud Collaborated Object Detection via Difficult-Case Discriminator

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 large data size of the intermediate results will introduce extravagant communication costs. To address this challenge, we propose a difficult-case based small-big model (DCSB) framework that deploys a difficult-case discriminator on the edge device to control the data transfer between the small model (edge) and the big model (cloud). Upon receiving data, the edge device operates a difficult-case discriminator to classify images into easy cases and difficult cases according to the specific semantics of the images. The difficult cases will be uploaded to the cloud. To reduce bandwidth consumption, we propose a regional sampling method that adaptively down-samples some regions of the difficult case to reduce the amount of transferred data based on the primary results of the lightweight model. Experimental results on VOC, COCO, and HELMET datasets using two object detection algorithms demonstrate that DCSB can detect 93.77%-97.05% objects but save 77.19% -80.55% of network bandwidth compared with the cloud-only method, while the edge-only method can only detect 54.90%-68.28% objects in the same condition. In addition, compared with the state-of-the-art model partition method - CAS, DCSB saves 95.19%-95.80% of the inference time when the transmission bandwidth is 8Mbps.