Introduction To YolactEdge For Real-time Object Segmentation On Edge Device

YolatEdge is one of the first competitive instanced segmentation techniques that can run on small devices with great real-time speed, It can reach up to 30fps on Nvidia Jetson AGX Xavier and 172fps on RTX 2080Ti. YolactEdge techniques come with Resnet-101 backbone which takes 550 550 resolution image as input. It paper called YolactEdge: Real-time Instance Segmentation on the Edge is authored by Haotian Liu, Rafael A. Rivera Soto, Fanyi Xiao, and Yong Jae Lee in Dec 2020, and the code and models are open-sourced on GitHub here. In order to do inferences in real-time speeds on edge devices, the authors built the SOTA image-based real-time instances segmentation method YOLACT and did some new changes mainly two: one at algorithms level and other system levels. YolactEdge leverages the facility of Nvidia TensorRT machine inference engine to quantize the network parameters to fewer bits while systematically balancing any tradeoff inaccuracy, and it also leverages temporal redundancy in the video, and learn to rework and propagate features over time in order that the deep network's expensive backbone feature computation doesn't get to be fully computed on every frame.