Convolutional Neural Network and LSTM for Seat Belt Detection in Vehicles using YOLO3
Abstract
The application of an electronic violation detection system has begun to be implemented in many countries using CCTV cameras installed at highway and toll road points. However, the development of a violation detection system using data in the form 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 belts, the use of cell phones while driving, which is influenced by the number of vehicles, vehicle speed and lighting, which can increase the difficulty in the detection process. This research developed a traffic violation detection system using a hybrid model, namely the CNN and LSTM algorithms for the application of discipline using seat belts. The dataset was obtained from RoboFlow Universe with a total of 199 front view car images consists of 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 the LSTM algorithm plays a role in the prediction process. Additionally, the performance evaluation of the CNN+LSTM algorithm will be measured using the accuracy value to measure the performance of the training process and testing process. When measuring the performance of the training process, it will be compared with several basic detection models used, such as CNN, VGG16, ResNet50, MobileNetV2, Yolo3, Yolo3+LSTM. The test results show that Yolo3+LSTM has a higher accuracy compared to the others, at 89%. Next, in the testing process, the CNN+LSTM model will be compared with the basic method, namely CNN. The test results show that the CNN+LSTM models have a higher accuracy of 89%. Meanwhile, in the basic CNN model, the resulting accuracy was 85%.
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References
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