Pedestrian detection using a translation-invariant wavelet residual dense super-resolution

Abstract

Pedestrian detection is an important research area and technology for car driving, gait recognition, and other applications. Although a lot of pedestrian detection techniques have been introduced, low-resolution imaging devices still exist in real life, so detection in low-resolution images remains a challenging problem. To address this issue, we propose a novel end-to-end Translation-invariant Wavelet Residual Dense Super-Resolution (TiWRD-SR) method to upscale LR images to SR images and then use Yolov4 for detection to address the low detection problem performance on low-resolution images. To make the enlarged SR image not only effectively distinguish the foreground and background of images but also highlight the characteristic structure of pedestrians, we decompose the image into low-frequency and high-frequency parts by stationary wavelet transform (SWT). The high- and low-frequency sub-images are trained through different network structures so that the network can reconstruct the high-frequency image edge information and the low-frequency image structure in a more detailed manner. In addition, a high-to-low branch information transmission (H2LBIT) is proposed to import high-frequency image edge information into the low-frequency network to make the reconstructed low-frequency structure more detailed. In addition, we also propose a novel loss function, which enables the SR network to focus on the reconstruction of image structure in the network by the characteristics of wavelet decomposition, thereby improving its detection performance. The experimental results indicate that the proposed TiWRD-SR can effectively improve detection performance.