Automatic Street Lighting System with Vehicle Detection using Deep-Learning Based Remote Sensing
Keywords:Vehicle Detection, Smart Light System, Remote Sensing, Computer Vision, Deep Learning, Convolutional Neural Networks, YOLO
Automated street lightning is advantageous to society as it decreases the rate of accidents, vandalism and street crimes. The ability to detect vehicles and smartly manage the street light system is among the major duties of electrical distribution companies. To recognize an objects of interest in an image by a classical technique called object detection. In order to recover the enactment and reduce the complexity of object detection, numerous computer vision methods have been proposed over the past decade years. Object detection has a wide variety of applications including vehicle detection especially in remote sensing applications. Vigorous and accurate detection of vehicles for such solicitations is a moderately stimulating problem because of discrepancy of color, size, aspect ratio and alignment of vehicles and complex backgrounds of satellite images. Nevertheless, the modern deep learning-based object detection frameworks and convolutional neural networks have great potential for improving the performance of vehicle detection methods in terms of exactness, sturdiness and detection time. This paper will review both conventional and modern object detection systems. Assortment of the supreme appropriate set of object sensor, mainstay story abstraction system, train-test proportion and hyper-parameters for recognition of automobiles for nifty street light system are studied in detail and compared by deep learning based object detectors Single shot multibox detector (SSD) and You Only Look Once (YOLO) and feature abstraction systems counting Alexnet, VGG-16, Resnet-18, Resnet-50. As between accuracy and detection time these methods offer several trade-off options. On mean average precision (mAP), precision vs, recall curves, computational complexity and time complexity is established by relative investigation.
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Copyright (c) 2022 Liwen Chen, Muhammad Shoaib Akram, Aafaq Saleem, Hidayat Ullah
This work is licensed under a Creative Commons Attribution 4.0 International License.