Adaptive Enhanced Detection Network for Low Illumination Object Detection

• Published in: Mathematics (Basel) Vol. 11; no. 10; p. 2404
• Main Authors: Wei, Hanting, Yu, Bo, Wang, Wei, Zhang, Chenghong
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2023
• Subjects: Algorithms; Analysis; artificial intelligence; Coders; Computational linguistics; Decoders; Deep learning; Equilibrium; Experiments; Illumination; Image enhancement; Language processing; Lighting; low illumination images; Medical research; Medicine, Experimental; Natural language interfaces; neural network; Neural networks; Object recognition; Optimization; risk factor detection; Risk factors; Teaching methods
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math11102404

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.