Vehicle detection and tracking in intelligent transportation systems based deep learning

Abstract

This research paper focuses on the advancements and optimizations made to fundamental object detection algorithms in vehicle detection. The study explores integrating and reusing CNN (Convolutional Neural Networks) models with other techniques to enhance performance. Three main models, namely Faster R-CNN (Faster Region-based Convolutional Neural Network), Improved SSD (Single Shot Multibox Detector), and YOLOv4 (You Only Look Once v4), are analyzed, showcasing their incremental improvements in accuracy and overall detection performance. However, the increased computational complexity and time demands are trade-offs. The study also presents EnsembleNet, a model combining Faster R-CNN and YOLOv5, which achieves higher average precision values. Another approach involves fusing edge features with CNN models, resulting in faster and more accurate vehicle recognition. The paper predicts future deep learning trends, emphasizing the need for improved hardware capabilities to handle complex models. Integrating deep learning with sensor fusion and edge computing holds promise for intelligent transportation systems.