A novel small-scale pedestrian detection method base on residual block group of CenterNet

Abstract

Pedestrian detection is widely used in intelligent supervision and assisted driving. With the development of deep learning, the accuracy of pedestrian detection has been greatly improved. In actual scenes, there are often pedestrians who are far away from the camera. Such pedestrians usually have small image sizes, while existing algorithms still have defects such as missed detection for similar small-scale pedestrian detection, which will reduce the accuracy of operation. Therefore, this paper designs a Three ResNet Blocks based on CenterNet detection model. Aiming at the limited ability of a single feature extraction block to extract semantic information at different levels in the network, this paper proposes Three ResNet Blocks, which is a simple and effective multi-block group. This block group integrates three different basic blocks, each of which extracts pedestrian information separately to enhance information flow in the network structure and make detection results more accurate. In addition, combined with the advantages of activation function in the model expression, the relu6 activation function is introduced to improve the performance of the detector by preventing numerical explosion being sensitive to decimal. Comprehensive experiments on pedestrian detection datasets (Caltech and ETH) show that the proposed method exhibits excellent accuracy and detection speed, especially for small-scale pedestrian detection.