Counting Various Vehicles using YOLOv4 and DeepSORT

Abstract

The Ministry of Public Works and Public Housing (PUPR) conducted a traffic survey to determine the total number of vehicles and classify them according to the Bina Marga vehicle categorisation. The survey has thus far been carried out manually. As a result, surveys take a lot of time and money to perform. Additionally, as the survey scope grows, so will the requirement for surveyors. Therefore, a substitute that can execute the survey procedure automatically and with tolerable accuracy is required. One solution is to utilise deep learning technology to detect and categorise vehicles that can be used in apps. The program is designed as a web application that provides a summary of vehicle calculations and receives video data from traffic recordings. The deep learning model used is YOLOv4 which is trained to recognise vehicle classes following Bina Marga vehicle types. The model was trained and tested using the Python programming language and the Darknet framework on the Google Colab platform. The YOLOv4 and DeepSORT method with custom dataset reached a decent accuracy of 67.94%, considering the limited 1000 images used for training the model.

Human Object Detection for Real-Time Camera using Mobilenet-SSD

“…The system will be created to capture image irritants, find people's positions and the number of people in a room. There are various object detection algorithms, such as YOLO, R-CNN, Mask R-CNN, MobileNet, and SqueezeDet [8,9,10,11,12]. This project will use the SSD (Single Shot Detector) method.…”