Open AccessComparative Analysis on Deep Learning Approaches for Heavy-Vehicle Detection based on Data Augmentation and Transfer-Learning techniques
Author(s) -
V. Sowmya,
R. Radha
Publication year - 2021
Publication title -
journal of scientific research
Language(s) - English
Resource type - Journals
eISSN - 2070-0245
pISSN - 2070-0237
DOI - 10.3329/jsr.v13i3.52332
Subject(s) - computer science , convolutional neural network , artificial intelligence , transfer of learning , deep learning , truck , pascal (unit) , machine learning , support vector machine , focus (optics) , object detection , intelligent transportation system , big data , pattern recognition (psychology) , data mining , engineering , physics , civil engineering , optics , programming language , aerospace engineering
Vehicle detection and recognition require demanding advanced computational intelligence and resources in a real-time traffic surveillance system for effective traffic management of all possible contingencies. One of the focus areas of deep intelligent systems is to facilitate vehicle detection and recognition techniques for robust traffic management of heavy vehicles. The following are such sophisticated mechanisms: Support Vector Machine (SVM), Convolutional Neural Networks (CNN), Regional Convolutional Neural Networks (R-CNN), You Only Look Once (YOLO) model, etcetera. Accordingly, it is pivotal to choose the precise algorithm for vehicle detection and recognition, which also addresses the real-time environment. In this study, a comparison of deep learning algorithms, such as the Faster R-CNN, YOLOv2, YOLOv3, and YOLOv4, are focused on diverse aspects of the features. Two entities for transport heavy vehicles, the buses and trucks, constitute detection and recognition elements in this proposed work. The mechanics of data augmentation and transfer-learning is implemented in the model; to build, execute, train, and test for detection and recognition to avoid over-fitting and improve speed and accuracy. Extensive empirical evaluation is conducted on two standard datasets such as COCO and PASCAL VOC 2007. Finally, comparative results and analyses are presented based on real-time.