Character Attentiveness Analysis Based on Deep Learning

In recent years, with the advancement of science and technology, people have begun to study computer vision security systems. Aiming at the pain points that affected safety by a single detection in the past, a feature centralized analysis system was designed. Through the combination of computer vision technology and deep learning, the fatigue detection and attention behavior detection are combined, the driver is photographed by the on-board camera, and the driving status of the driver is detected from multiple angles and multiple levels, so as to provide comprehensive guarantee for people to travel. This paper is divided into four modules, namely the collection and labeling of image data, the training of the Yolo model, the design of the character set analysis algorithm based on deep learning, and the display of the character set analysis system. Through the use and display of the system, the driver's fatigue state and the driver's distracted behavior during driving can be well identified, and corresponding alarms will be issued. The system has high precision and relatively stable operation. It can detect the driver's attention in real time, protect the driver's driving safety, and reduce the occurrence of traffic accidents, which has important practical significance.