การพัฒนาระบบตรวจจับผู้ขับขี่รถจักรยานยนต์ที่ไม่สวมหมวกนิรภัย

Abstract

The most common risky behaviors on the road are speeding, drunk driving, not wearing a helmet or seatbelt, not turning on motorcycle headlights, and disobeying traffic signals. We will focus on the risky behavior of not wearing a helmet. To reduce the risk of accidents, this research is conducted to develop an artificial intelligence and machine learning system that can detect riders who do not wear helmets and to analyze and compare the capabilities of YOLO and RetinaNet algorithms in detecting these riders. The data from the CMU Smart Gate system's LPR (License Plate Recognition) camera, which detects the data of vehicles entering and exiting the gates of Chiang Mai University, was used for training and measuring the system performance. The results showed that both YOLO and RetinaNet algorithm could be used to develop a system to detect motorcyclists who do not wear helmets. However, the RetinaNet algorithm training model mean precision of 0.999 was higher than that of the YOLO algorithm which is 0.983. Precision specific to detecting motorcyclists without helmets both algorithms got the same result of 1.000. When the model was tested for processing time per image, the YOLO algorithm took less time to execute than the RetinaNet algorithm. At average value, the YOLO algorithm took 0.152 seconds. The RetinaNet algorithm took 1.659 seconds.