International Journal For Multidisciplinary Research

E-ISSN: 2582-2160     Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 6 Issue 1 January-February 2024 Submit your research before last 3 days of February to publish your research paper in the issue of January-February.

Detection of Traffic Control Devices during Bridge Construction Projects Using Deep Learning

Author(s) M. Parvathi
Country India
Abstract Recent days during the time of road construction so many accidents may occur due to the objects in the construction area cannot capture during travel. Some method must be implemented to classify and locate instances in the image. In deep learning area it extends its application to this field for object detection. Even though a method is identified it has risk factors such as real-time detection, changeable weather, and complex lighting conditions. In this paper we are discussing the algorithm used for detection and a next version of that algorithm to detect the object with more precision. And also the number frames capture during driving is also more than other algorithm. Here we discussed about YOLO algorithm, and which is the best algorithm that applies different aspects such as key generic object detection frameworks, categorized object detection applications in traffic scenario, evaluation metrics, and classified datasets are included. The idea in YOLO v7 is to avoid that there is an identity connection when a convolutional layer with residual or concatenation is replaced by re-parameterized convolution.
Keywords YOLO, evaluation metrics, deep learning, convolution
Field Computer Applications
Published In Volume 6, Issue 1, January-February 2024
Published On 2024-01-30
Cite This Detection of Traffic Control Devices during Bridge Construction Projects Using Deep Learning - M. Parvathi - IJFMR Volume 6, Issue 1, January-February 2024. DOI 10.36948/ijfmr.2024.v06i01.11878
DOI https://doi.org/10.36948/ijfmr.2024.v06i01.11878
Short DOI https://doi.org/gtghn3

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