The implementation of intelligent systems in automating vehicle detection on the road

Susanto Susanto, Dimas Dwi Budiarjo, Aria Hendrawan, Prind Triajeng Pungkasanti


Indonesia is a country with a high population, especially in big cities. The road always crowded with various types of vehicles. Sometimes the growth of vehicles is not matched by road construction. During peak hours, too many vehicles can cause traffic jams on the road. The road is needed to be widened to accommodate the number of vehicles that pass each day. In order for road widening to be precise at locations that frequently occur in traffic jams, data on the number and classification of vehicles passing is required. Therefore, a system that can calculate and recognize the type of vehicle that passes is needed. The development of various studies on artificial intelligence especially about object detection can classify and calculate the type of vehicle. In this study, the authors used the YOLO object detection system using a convolution neural network (CNN) method to classify and count vehicles that pass automatically. The author uses a dataset of 600 images with 4 classes which are car, truck, bus, and motorbikes that pass through the road. The results showed that the YOLO object detection system can recognize objects consistently with accuracy more than 80% on CCTV video that installed on the road.


Artificial intelligence; Convolutional neural network; Object detection

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