Implementation of You Only Look Once Version 8 Algorithm to Detect Multi-Face Drivers and Vehicle Plates

Abstract

Checking the identity of motorcycle owners when leaving the college area is a mandatory activity for security officers to ensure that vehicles entering and exiting the college are the same driver. The conventional checking process often causes the impact of vehicle queues when the volume of vehicles increases. Therefore, an intelligent system is needed to detect multi-plate vehicles automatically. One approach in the world of image detection of an object is the use of the YOLO (You Only Look Once) algorithm. This algorithm predicts bounding boxes and possible classes in a single frame. This research divides objects into 3 classes, namely vehicles, driver's faces, and vehicle plates. The dataset used was 74 varied images consisting of 50 training data, 12 validation data and 12 testing data. The image was trained using 300 epochs and a batch size of 8 and resulted in an F1 score calculation for detecting objects reaching 92%.

Keywords

• driver's faces, multi-face, vehicle plates