Driver Cell-phone Use Detection Based on CornerNet-Lite Network

Abstract

Driver requires a high level of concentration when driving at high speed. Cell-phone use while driving can cause serious problems. To solve this issue, this paper proposes a proposal scheme about driver cell-phone use detection based on CornerNet-Lite Network. The scheme can eliminate the traffic accident risk caused by cell-phone use through detecting driver cell-phone use. The scheme includes two stages: model training and simulation test. Firstly, the data set of cell-phone use was established. Secondly, the data set was preprocessed by the preset processing method. Finally, the CornerNet-Lite network architecture was optimized to reduce the training time and improve the detection accuracy and real-time detection. Through a large number of experiments, the results showed that the scheme had a good detection effect, with the accuracy of 86.2% and with 30fps. Under the noise interference of simulated cab environment, it still had a high robustness.