Adaptive Enhanced Detection Network for Low Illumination Object Detection

Hanting Wei, Bo Yu (), Wei Wang and Chenghong Zhang
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Hanting Wei: University of Chinese Academy of Sciences, Beijing 100049, China
Bo Yu: University of Chinese Academy of Sciences, Beijing 100049, China
Wei Wang: Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China
Chenghong Zhang: University of Chinese Academy of Sciences, Beijing 100049, China

Mathematics, 2023, vol. 11, issue 10, 1-17

Abstract: Any small environmental changes in the driving environment of a traffic vehicle can become a risk factor directly leading to major safety incidents. Therefore, it is necessary to assist drivers in automatically detecting risk factors during the driving process using algorithms. However, besides making it more difficult for drivers to judge environmental changes, the performance of automatic detection networks in low illumination scenarios can also be greatly affected and cannot be used directly. In this paper, we propose a risk factor detection model based on deep learning in low illumination scenarios and test the optimization of low illumination image enhancement problems. The overall structure of this model includes dual discriminators, encoder–decoders, etc. The model consists of two main stages. In the first stage, the input low illumination scene image is adaptively converted into a standard illumination image through a lighting conversion module. In the second stage, the converted standard illumination image is automatically assessed for risk factors. The experiments show that the detection network can overcome the impact of low lighting and has high detection accuracy.

Keywords: neural network; risk factor detection; low illumination images; artificial intelligence; deep learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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