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

IF 8.1 1区 计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS Information Sciences Pub Date : 2024-11-04 DOI:10.1016/j.ins.2024.121612
Wei-Yen Hsu , Chun-Hsiang Wu
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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.
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用于行人检测的小波结构-纹理感知超分辨率
本研究旨在利用超分辨率技术解决在低分辨率(LR)图像中检测行人的难题。所提出的小波结构纹理感知超分辨率(WSTa-SR)方法是一种新颖的端到端解决方案,它将低分辨率图像放大为高分辨率图像,并采用 Yolov7 进行检测,有效解决了检测性能低的问题。首先利用静态小波变换(SWT)将 LR 图像分解为低频和高频子图像,然后由不同的子网络进行处理,通过强调行人特征来更准确地区分行人和背景。此外,还提出了一种高-低信息传递机制(H2LID 机制),用于传递高频细节信息,以增强低频结构的重建。此外,还引入了一种利用小波分解特性的新型损失函数,以进一步提高网络在图像结构重建和行人检测方面的性能。实验结果表明,所提出的 WSTa-SR 方法能有效提高行人检测率。
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来源期刊
Information Sciences
Information Sciences 工程技术-计算机:信息系统
CiteScore
14.00
自引率
17.30%
发文量
1322
审稿时长
10.4 months
期刊介绍: Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions. Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.
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