Fatigued driving is a significant contributor to traffic accidents. There are some issues with common EEG data of 32 channels, 64 channels, and 128 channels, such as difficult acquisition, high data redundancy, and difficult practical application. A new channel selection method called ReliefF_SFS is proposed to address the problem of how to reduce the number of channels while maintaining classification accuracy. It combines the ReliefF algorithm and the sequential forward selection (SFS) algorithm. When only T6, O1, Oz, T4, P3, and FC3 are used, the classification accuracy under Theta_Std+FE combined with ReliefF_SFS achieves 99.45%. The strategy suggested in this paper not only ensures the recognition accuracy but also reduces the number of channels when compared to other models based on the same data set.

Most of the current water meters are manually carried with a palm-integrated machine to read the meters on the spot, which has low labor efficiency and high labor costs, which leads to problems such as low efficiency for the entire water department. In response to this problem, this article aims to in-depth study and analyze a suitable water meter image recognition model to improve labor efficiency, reduce labor costs, and thereby improve the overall efficiency of the water department. The main content of this paper is to select a model with high recognition accuracy for the numbers in the black rectangle. This paper first introduces the existing in-depth learning models, such as Faster RCNN, SSD, and YOLOv3. Then two datasets are sorted out, one is the original water table picture dataset, the other is the data set cut out of a black rectangle box. Then two plans are proposed, one is to train the whole picture directly and recognize the water meter digits through the in-depth learning network, the other is to train the black rectangle picture and recognize the water meter digits through the in-depth learning network. Finally, by comparing the three models from different angles, it is determined that YOLOv3 in plan B has the best recognition effect, and the accuracy rate reaches 90.61%, which can greatly improve the labor efficiency and save labor costs.