- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
- Chinese S&T Journal Citation Reports
- Chinese Science Citation Database
| Citation: | WANG Yin, WANG Lide, QIU Ji. Real-Time Enhancement Algorithm Based on DenseNet Structure for Railroad Low-Light Environment[J]. Journal of Southwest Jiaotong University, 2022, 57(6): 1349-1357. doi: 10.3969/j.issn.0258-2724.20210199
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Train on-board vision system is an important guarantee for the safety of future urban rail transit operations. The detection effect of the on-board vision system will be seriously affected by the low-light environment when the train operates in a closed environment or at night. To this end, a real-time visual enhancement algorithm is proposed for low-light images in a closed railway environment or night driving environment. The algorithm uses a densely connected network (DenseNet) structure as the backbone network to establish a feature-size invariant network. The network extracts image illumination, color, and other information and predicts the light enhancement rate images. These rate maps adjust the light intensity of each pixel on the basis of the nonlinear mapping function. The network enhances the exposure rate of low-light input images through a hierarchical structure from low level to high level. The developed deep learning network model uses self-supervised learning to train the network parameters. The chracteristics of the low-light image and the prior knowledge are utilized to construct the loss function, which consist of three components: exposure loss, colour constancy loss and illumination smoothness loss. The experimental results of low-light enhancement in multiple scenes show that the algorithm can adapt to the exposure value of input images, dynamically adjust the exposure rate for low-exposure and high-exposure regions to improve the visualization of low-light images, and the processing speed can reach 160 fps to meet the requirements of real-time processing. The comparative experiments of railroad segmentation and pedestrian detection before and after low-light enhancement prove that the proposed algorithm can improve the visual detection in a low-light environment. As for testing on the RSDS (railroad segmentation dataset) datasets, the
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