DETECTION OF VEHICLE TYPE AND LICENSE PLATE WITH CONVOLUTIONAL NEURAL NETWORK MODEL YOLOV7 - Suhartono, Suhartono; Zain, Satria Gunawan; Ardilla, Andi (Jurnal Teknik Informatika (Jutif), 2024)
This research was conducted in response to issues related to the efficiency and effectiveness of vehicle type and license plate detection. The increasingly congested traffic conditions and the expanding use of motor vehicles have posed challenges in traffic monitoring and regulation. Therefore, there is a need to develop a solution that can save time and resources while providing more comprehensive information in vehicle monitoring. This research implements the Convolutional Neural Network (CNN) algorithm with the latest YOLOv7 model from YOLO to detect vehicle types and vehicle number plates simultaneously to make it more efficient and effective, save time and resources, and provide more complete information. The research method used is Research and Development (R&D) with an experimental approach. The stages include image acquisition, labeling, dataset sharing, YOLOv7 model training, testing, prediction results, and conversion to text using Optical Character Recognition (OCR). The research results show that the ResNet34 model architecture achieves a total accuracy of 89.7% for 3x3 convolution layers and 88.6% for 5x5 convolution layers. The YOLOv5 architecture performs well on 3x3 convolution layers with an overall accuracy of 71.9%, and 58.3% for 5x5 convolution layers. However, the YOLOv7 and Mobilenet architectures tend to have lower accuracy, namely the Mobilenet architecture with a 3x3 convolution layer with a total accuracy of 63.4%, and 73.4% for the 5x5 convolution layer. Computing speed is also considered, with YOLOv5 and YOLOv7 having higher speeds than ResNet34 and Mobilenet. Tests were carried out in various lighting conditions, resulting in accurate detection of vehicle types and vehicle number plates of 90% in the morning, 85% in the afternoon and 77% at night. Overall, the system succeeded in recognizing objects with an accuracy of 84% from a total of 720 data tested, but the accuracy of converting vehicle number plates using OCR reached 22%. The results of this research demonstrate the performance and effectiveness of the YOLOv7 algorithm in detecting vehicle types and vehicle number plates, as well as providing insight into accuracy in various lighting conditions and OCR conversion.