Impact of Light Flickering on Object Detection Accuracy using Convolutional Neural Networks

Carvalho, Samuel; Humphries, Jacqueline; Dunne, Nathan; Leahy, Shauna

doi:10.1109/conftele50222.2021.9435506

Cited by 3 publications

(1 citation statement)

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“…Within the scope of the study, it was worked with 30 FPS (Frames Per Second) imaging speed. It has been observed that a CNN model trained for object detection can successfully detect under 280 lumens of light [22]. The amount of illumination available in the indoor environment with ideal illumination of the study was measured as 300 lumens with a luxmeter.…”

Section: Model Definitionmentioning

confidence: 99%

Solution of Real-Time Traffic Signs Detection Problem for Autonomous Vehicles by Using YOLOV4 And Haarcascade Algorithms

Ortatas

Çetin

2023

International Journal of Automotive Science and Technology

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Unmanned systems are increasingly used today to facilitate our daily lives and use time more efficiently. Therefore, this rapidly emerging and growing technology appears in every aspect of our lives with its various functions. Object recognition algorithms are one of the most important functions that we often encounter in these systems. Autonomous vehicle technologies are the latest and fastest-growing technology among unmanned systems. In this study, we investigate the success rates of two different algorithms for recognizing traffic signs and markings that can be used for partially or fully autonomous vehicles. In this study, two different solutions to the problem of recognizing the signs for fully autonomous and fully autonomous vehicles, respectively, were presented and the correct identification of the markers was evaluated. The work was performed in real-time. Two different con-cepts were used for these products. An enclosed space where an ideal lighting environment is provided for the evaluation of models should be visualized. In addition, for the general recognition of the models, the test procedures were performed with a dataset obtained from the users and it was computed for the general recognition. In addition, this study aims to provide a better understanding of the basic working principles, the differences between ma-chine learning and deep learning, and the contents of object recognition processes.

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Section: Model Definitionmentioning

confidence: 99%