TFDet: Target-Aware Fusion for RGB-T Pedestrian Detection

• URL: http://arxiv.org/abs/2305.16580v3
• Date: Wed, 18 Oct 2023 01:45:06 GMT
• Title: TFDet: Target-Aware Fusion for RGB-T Pedestrian Detection
• Authors: Xue Zhang, Xiao-Han Zhang, Jiacheng Ying, Zehua Sheng, Heng Yu, Chunguang Li, Hui-Liang Shen
• Abstract summary: We propose a novel target-aware fusion strategy for multispectral pedestrian detection, named TFDet. Our fusion strategy highlights the pedestrian-related features and suppresses unrelated ones, generating more discriminative fused features. TFDet achieves state-of-the-art performance on both KAIST and LLVIP benchmarks, with an efficiency comparable to the previous state-of-the-art counterpart.
• Score: 21.502127701404792
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Pedestrian detection plays a critical role in computer vision as it contributes to ensuring traffic safety. Existing methods that rely solely on RGB images suffer from performance degradation under low-light conditions due to the lack of useful information. To address this issue, recent multispectral detection approaches have combined thermal images to provide complementary information and have obtained enhanced performances. Nevertheless, few approaches focus on the negative effects of false positives caused by noisy fused feature maps. Different from them, we comprehensively analyze the impacts of false positives on the detection performance and find that enhancing feature contrast can significantly reduce these false positives. In this paper, we propose a novel target-aware fusion strategy for multispectral pedestrian detection, named TFDet. Our fusion strategy highlights the pedestrian-related features and suppresses unrelated ones, generating more discriminative fused features. TFDet achieves state-of-the-art performance on both KAIST and LLVIP benchmarks, with an efficiency comparable to the previous state-of-the-art counterpart. Importantly, TFDet performs remarkably well even under low-light conditions, which is a significant advancement for ensuring road safety. The code will be made publicly available at https://github.com/XueZ-phd/TFDet.git.

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