Efficient Vehicle Detection Using Deep Learning

Author(s)	Dhinakaran J, Dinesh A, Jayaraj J, Manoj S
Country	India
Abstract	In intelligent transportation systems, the suggested system uses a convolutional neural network (CNN) for object identification and classification. By using data augmentation approaches to improve model generalization and adjust to variables like weather and occlusions, it fine-tunes a pre-trained CNN model using a large annotated dataset that captures a variety of traffic events. Based on distinguishing characteristics, the CNN model is trained for multiclass classification, classifying automobiles. In an effort to increase precision and dependability in practical situations, this system provides a strong solution to the problems associated with object detection in intricate traffic situations.
Keywords	Vehicle Detection, CNN-Based Classification, Intelligent Transportation, Object Recognition, Real-Time Analysis.
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 6, Issue 1, January-February 2024
Published On	2024-02-24
Cite This	Efficient Vehicle Detection Using Deep Learning - Dhinakaran J, Dinesh A, Jayaraj J, Manoj S - IJFMR Volume 6, Issue 1, January-February 2024. DOI 10.36948/ijfmr.2024.v06i01.9036
DOI	https://doi.org/10.36948/ijfmr.2024.v06i01.9036
Short DOI	https://doi.org/gtjt3z

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