RESIDUAL NETWORK LAYER COMPARISON FOR SEAT BELT DETECTION
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Irma Amelia Dewi(1*)
Institut Teknologi Nasional Bandung
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Nur Zam Zam Nasrulloh(2)
Institut Teknologi Nasional Bandung
(*) Corresponding Author
Abstract
Most of the monitoring of traffic violations on Indonesian roads is currently done manually by monitoring through CCTV cameras, so drivers still have the possibility of violating the use of seat belts. Residual Network (ResNet) as one of the architectures with an accuracy rate of up to 96.4% in 2015, which is intended to overcome the vanishing gradient problem that commonly occurs in networks with many layers. Therefore, in this study, a system was developed using the RetinaNet architecture to detect drivers who use seat belts and drivers who do not use seat belts with the ResNet backbone. In addition, this study compares the performance of ResNet-101 and ResNet-152. The hyperparameters used include a dataset of 10,623 images in the training process, and the batch size parameter is 1, with a total of 10,623 steps, and the number of epochs is 16. Based on 60 tests conducted in this study, the RetinaNet model with the ResNet-152 architecture performed better than the ResNet-101 architecture. The ResNet-152 architecture resulted in a system performance with an accuracy of 98%, precision value of 99%, recall value of 99%, and an f1 score of 99%.
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