Pendeteksian Lubang Pada Jalanan Menggunakan Metode SSD-MobileNet

The rapid advancement of technology following the number of potholes on the streets that need to be inspected have led people to develop technology that can inspect pothole using a detection system. Digital image processing is a method used by some people to detect potholes by using its colour as the main extracted feature, after that the field of machine learning and deep learning approaches have been studied and developed in terms of detection, one of which is the ssd-mobilenet. In this study three types of dataset were used, they were obtained secondarily from various sources, namely the normal dataset, the dashboard dataset, and the closeup dataset. These three datasets will also be combined and varied in the amount of the training data with an increment of 500 data train so that various model results are obtained. The results obtained are the detection bounding boxes and also the confusion matrix score of each model dataset, where the normal dataset gets an accuracy score of 56%, the dashboard dataset gets 50% and the closeup dataset gets 76%.