Prof. Zhiyu Zhu Profile

Prof. Zhiyu Zhu

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Proceedings Article | 13 June 2024 Paper

RGB-T pedestrian detection method based on improved illumination-aware weight

Hao Fu, Zhiyu Zhu

Proceedings Volume 13180, 131802P (2024) https://doi.org/10.1117/12.3033557

KEYWORDS: Light sources and illumination, Feature fusion, Detection and tracking algorithms, Semantics, Object detection, Infrared radiation, Visible radiation, Education and training, Infrared imaging, Image segmentation

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Aiming to address the issues of low recognition accuracy, limited all-weather usability, and high missed detection rate in RGB-T multimodal pedestrian detection algorithms, this paper proposes an improved illumination-aware weight-based RGB-T pedestrian detection method. Firstly, the mid-term fusion strategy is adopted by changing the fusion point from after five layers of convolution to after the third layer of convolution. This ensures that the fused feature map contains both detailed and semantic features. Secondly, through learnable parameters, the illumination weights w_d and w_n are adjusted to more reasonably map illumination information to fusion weights, resulting in new illumination weights w_r and w_t. Finally, adjustments are made to the network structure of the semantic segmentation module by replacing two-stream semantic segmentation with fused single-stream semantic segmentation using new illumination weights w_r and w_t. The proposed method's detection ability is validated on a cleaned KAIST dataset. Experimental results demonstrate that compared to the original network model, our proposed method reduces average missed detection rate from 15.69% to 13.15% while improving mAP@0.5 by 4.1%, showcasing superior performance.

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