Heavy-loaded Vehicles Detection Model Testing using Synthetic Dataset

Abstract

Currently, many roads in Indonesia are damaged. This is due to the presence of large vehicles and large loads that often pass. The more omissions are carried out, the more damaged and severe the road is. The central government and local governments often carry out road repairs, but this problem is often a problem. Damaged roads are indeed many factors, one of which is the road load. The road load is caused by the number of vehicles that carry more than the specified capacity. There are many methods used to monitor roads for road damage. The weighing post is a means used by the government in conducting surveillance. This research is not a proposal to monitor the road, but this is only to create a model for the purpose of detecting heavily or lightly loaded vehicles. This research is to classify using Convolutional Neural Network (CNN) with pre-trained Resnet50. The model generated from the Convolutional Neural Network training process reaches above 90%. Generate Image deep learning algorithms such as the Generative Adversarial Network currently generate a lot of synthetic images. The testing dataset that will be used is generated from style transfer. The model is tested using a testing dataset from the generated style transfer. Style transfer is a method of generating images by combining image content with image styles. The model is pretty good at around 92% for training and 88% for testing, can it detect image style transfer? The Convolutional Neural Network model is said to be good if it is able to recognize the image correctly, considering that the accuracy of the model is very good. One of the reasons why the training model is good but still makes errors during testing, then the image dataset is overfitting