Wavelet structure-texture-aware super-resolution for pedestrian detection

Abstract

This study aims to tackle the challenge of detecting pedestrians in low-resolution (LR) images by using super-resolution techniques. The proposed Wavelet Structure-Texture-Aware Super-Resolution (WSTa-SR) method is a novel end-to-end solution that enlarges LR images into high-resolution ones and employs Yolov7 for detection, effectively solving the problems of low detection performance. The LR image is first decomposed into low and high-frequency sub-images with stationary wavelet transform (SWT), which are then processed by different sub-networks to more accurately distinguish pedestrian from background by emphasizing pedestrian features. Additionally, a high-to-low information delivery mechanism (H2LID mechanism) is proposed to transfer the information of high-frequency details to enhance the reconstruction of low-frequency structures. A novel loss function is also introduced that exploits wavelet decomposition properties to further enhance the network’s performance on both image structure reconstruction and pedestrian detection. Experimental results show that the proposed WSTa-SR method can effectively improve pedestrian detection.