Vehicle detection in the infra-red thermal images based on a sparse neural network

Xunxun Zhang; Lang Peng; Xiaoyu Lu

doi:10.1117/12.2680753

27 June 2023 Vehicle detection in the infra-red thermal images based on a sparse neural network

Xunxun Zhang, Lang Peng, Xiaoyu Lu

Proceedings Volume 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022); 1270502 (2023) https://doi.org/10.1117/12.2680753
Event: Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 2022, Nanjing, China

Abstract

Recently, infra-red thermal images have drawn wide publicity for vehicle detection in traffic monitoring because of their resistances to insufficient light and hidden occasions. However, there are two significant challenges compared with vehicle detection in visible images. One is the insufficiency of the labelled infra-red thermal samples, and the other is less texture information. These two points lead to fewer parameters for the vehicle detection network in the infra-red thermal images. Therefore, to conquer these difficulties in infra-red thermal images, we construct a sparse neural network based vehicle detection method with sparse parameters (weights) and high generalization ability. Therein, to avoid over-fitting in the training stage, we eliminate some redundant parameters via constraints and on-line pruning. Finally, to verify the proposed vehicle detection method in infra-red thermal images, we introduce VIVID, VOT2019, and RGBT234 data sets. The vehicle detection results show that the proposed method presents superior results compared to several mainstream approaches.

Citation Download Citation

Xunxun Zhang, Lang Peng, and Xiaoyu Lu "Vehicle detection in the infra-red thermal images based on a sparse neural network", Proc. SPIE 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 1270502 (27 June 2023); https://doi.org/10.1117/12.2680753

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KEYWORDS

Thermography

Neural networks

Education and training

Overfitting

Convolution

Optical spheres

Target detection

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