Convolutional Neural Network and LSTM for Seat Belt Detection in Vehicles using YOLO3

Convolutional Neural Network and LSTM for Seat Belt Detection in Vehicles using YOLO3

intelligent systems; seat belt violation detection; yolo; convolutional neural networks, LSTM

The application of an electronic violation detection system has begun to be implemented in many countries by utilizing CCTV cameras installed at highway and toll road points. However, the development of a violation detection system using data in theform of images that have a high level of accuracy is still a challenge for researchers. Several types of violations detected include the use of seat beltsand the use of cell phones while driving which is influenced by the number of vehicles, vehicle speed and lightingwhich can increase the difficulty in the detection process. This research developed a traffic violation detection system usingYOLO3. The YOLOis used as the basic architecture of CNN which is then combined withLSTM. The dataset was obtained from RoboFlow Universe with a total of 199 front-viewcar images consistingof 82 using seatbelts and 78 not using seatbelts for the training process. The CNN algorithm plays a role in the feature extraction process from input image data, while LSTM plays a role in the prediction process. Furthermore, the performance evaluation of the CNN+LSTM algorithm will be measured using the value of accuracy to measure the performance of the training process and testing process. In measuring the performance of the training process,it will be compared with several basic detection models used, such as CNN, VGG16, ResNet50, MobileNetV2, YOLO3, and YOLO3+LSTM. The test results show that YOLO3+LSTM has higher accuracy compared to the others at 89%. Next, in the testing process, the CNN+LSTMmodel will be compared with the basic method, namely CNN. The test results show that the CNN+LSTM models havehigher accuracy at 89%. Meanwhile, in the basic CNN model, the resulting accuracy was 85%

Erika Devi Udayanti1,Etika Kartikadarma2, Fahri Firdausillah

https://jurnal.iaii.or.id/index.php/RESTI/article/view/5784/936

Facultyof Computer Science, Universitas Dian Nuswantoro, Semarang, Indonesia

04-06-2024

FAJAR BAGUS W

PDF

ENGLISH

TEXT