Research on mental judgment techniques based on deep learning

Fatigue driving is an important cause of traffic accidents. Compared with other traffic accidents, traffic accidents caused by fatigue driving have a higher fatality rate and greater damage to the surrounding environment. Therefore, many cars manufactured by automobile companies are equipped with driver assistance functions as driver assistance, and many third-party companies are also following up to manufacture fatigue detection equipment. Although many companies and researchers have made a lot of achievements in the field of fatigue detection, there are still many areas for improvement in the research of fatigue detection. The methods of fatigue detection on the market are generally divided into invasive detection and non-invasive detection. In this paper, a non-invasive fatigue detection system based on deep learning is proposed. The system conducts face detection by YOLOv5 algorithm. At the same time, 12 key points in the eyes and 5 key points in the mouth are added to the recognized faces to detect facial expression changes, and on this basis, PERCLOS algorithm is used as the judgment basis. The fatigue state of the current face is evaluated.When epoch=80, mAP@0.5(mean accuracy) can reach 0.995, mAP@0.5:.95(the weighted average of mean accuracy under different IoU thresholds, generally speaking, the higher the accuracy, the better), also reaches 0.81.