A Combined Multi-Mode Visibility Detection Algorithm Based on Convolutional Neural Network

The accuracy of visibility detection greatly affects daily life and traffic safety. Existing visibility detection methods based on deep learning rely on massive haze images to train neural networks to obtain detection models, which are prone to overfit in dealing with small samples cases. In order to overcome this limitation, a large amount of measured data are used to train and optimize the convolutional neural network, and an improved DiracNet method is proposed to improve the accuracy of the algorithm. On this foundation, combined multi-mode algorithm is proposed to achieve small samples fitting and train an effective model in a short time. In this paper, the proposed improved DiracNet and the combined multi-mode algorithm are verified by using the measured atmospheric fine particle concentration data (pm1.0, pm2.5, pm10) and haze video data. The validation results demonstrate the effectiveness of the proposed algorithm.