Pedestrian Detection Using Multi-Scale Structure-Enhanced Super-Resolution

Abstract

Pedestrian detection remains a crucial technology for applications such as autonomous driving and gait recognition and continues to be a prominent research area. Despite the development of advanced pedestrian detection techniques, the challenge of detecting pedestrians in low-resolution images persists in real-life scenarios where low-quality imaging devices are still in use. The objective of this study is to enhance the detection of pedestrians in low-resolution (LR) images by improving image quality through super-resolution techniques. To achieve this goal, we propose an end-to-end Multi-scale Structure-Enhanced Super-Resolution (MsSE-SR) method to enlarge LR images into high-resolution (SR) images and utilize Yolov4 for detection, effectively addressing the issue of low detection performance in LR images. To generate an SR image that can accurately distinguish between foreground and background elements while emphasizing pedestrian characteristics, we employ the stationary wavelet transform (SWT) to decompose the image into low and high-frequency sub-images. These sub-images are then processed through different network structures, enabling the network to reconstruct high-frequency details and low-frequency structures with greater precision. Moreover, we propose a high-to-low subnetwork information transfer (H2LSnIT) that incorporates high-frequency edge information into the low-frequency image structure during the reconstruction process, leading to a more detailed reconstruction of the low-frequency structure. We also propose a novel loss function that leverages the characteristics of wavelet decomposition to enhance the network’s focus on reconstructing the image structure, further improving detection performance. The experimental results demonstrate the effectiveness of the proposed MsSE-SR method in significantly enhancing pedestrian detection performance.

Topics & Concepts