Attention Retinex Network(A4R-Net) for face detection under low-light environment

Abstract

The degradation of recognition rates in low-light environments is a critical issue in terms of security when using object and face recognition technologies in various locations. Existing low-light enhancement models have shown limitations in terms of computational cost and performance. However, this paper overcomes these limitations. The experimental results demonstrate that our model achieves the same performance as existing models with 13 times lower computational cost and a face detection performance of 82.2%.The structure of this paper is as follows: Introduction, which provides the background and explains the limitations of existing models. Proposed Method, which details the structure and working principles of A4R-Net. Experimental Results, which present the evaluation of low-light enhancement performance and the comparison of face detection using YOLOv4 [1]. Conclusion, which discusses the contributions of this research and future research directions.The source code and dataset is https://github.com/Obiru2698/obiru2698.github.io/

Keywords

• Low-light enhancement
• Attention mechanism
• Face detection