A LIGHTWEIGHT HIGH-RESOLUTION REPRESENTATION BACKBONE FOR REAL-TIME KEYPOINT-BASED OBJECT DETECTION

The keypoint based detectors are a relatively new object detection mechanism, avoiding the complicated computation related to anchor box and achieving state-of-the-art accuracy. However, inference speed is a major drawback of these detectors because of the heavy backbone network. In this paper, we design a novel lightweight backbone named DNet for keypoint-based detection and propose a real-time object detection network. In the backbone part, DNet is able to maintain high-resolution feature maps throughout the process and gradually extract and integrate features across scales. In the detection part, we detect a center keypoint and a pair of corners to predict the bounding boxes, and completely avoid the complicated computation related to anchor boxes. Compared with state-of-the-art real-time detectors, our network achieves superior performance with 30.0% AP on COCO benchmark at 21.5ms. In addition, the experimental results show that our network is capable of running real-time on embedded devices.