EvaluatingFasterR-CNNandYOLOv8forTrafficObject Detection andClass-Based Counting
Journal Title: International Journal of Innovations in Science and Technology - Year 2024, Vol 6, Issue 4
Abstract
Real-time traffic object detection is a critical component necessary for achieving a fully autonomous traffic system. Traffic object detection, along with background classification, is a significant area of research aimed at enhancing safety on the roads and reducing accidents by accurately identifying vehicles. This research aims to develop an accurate and efficient system for traffic object detection and classification in real-time traffic environments. It also seeks to minimize false positives and negatives, ensuring that no objects are overlooked in the detection of classes such as cars, buses, bicycles, motorcycles, and pedestrians. This research aims and focuses on the two following deep learning technologies: YOLO stands for (You Only Look Once) and Faster R- CNN stands for (Region-based Convolutional neural network). YOLO, initially designed as the single-stage approach, emphasizes speed; therefore, it is best suited for real-time uses. However, Faster R-CNN which is a two-stage detector gives better results in object detection and is highly accurate. Both models are trained and tested on the same data set containing 5712 trained images, 570 validation images, and 270 test images using a workstation with RAM 32 GB and NVIDIA GeForce RTX 4080 Super GPU through the help of CUDA version 12.4 to provide the end evaluating results. Since Faster RCNN is a very intensive model it took 22 hours to complete 3 epochs with an accuracy of 55.2% to train the model and YOLO finished the training within 10 epochs with the mAP@0.5 value of 0.931 of all classes. Our results of traffic object real-time detection indicated that YOLO was vastly better and quicker than Faster R-CNN.
Authors and Affiliations
Muhammad Talha Jahangir, Tahreem Fatima, Qandeel Fatima
Keywords
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