Classification & Detection of Vehicles using Deep Learning
Madde Pavan Kumar1, K.Manivel2, S.Ponlatha3, N. Jayanthi4

1MaddePavan Kumar, ECE, Mahendra Engineering College, Namakkal, India.
2Dr.K.Manivel, Associate Professor/ECE, Mahendra Engineering College, Namakkal, India.
3Dr.S.Ponlatha, Associate Professor, ECE, Mahendra Engineering College, Namakkal, India.
4N.Jayanthi, Assistant Professor, ECE, Mahendra Engineering College, Namakkal, India.
Manuscript received on April 20, 2020. | Revised Manuscript received on April 30, 2020. | Manuscript published on May 10, 2020. | PP: 1033-1040 | Volume-9 Issue-7, May 2020. | Retrieval Number: F3905049620/2020©BEIESP | DOI: 10.35940/ijitee.F3905.059720
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Abstract: The vehicle classification and detecting its license plate are important tasks in intelligent security and transportation systems. However, the existing methods of vehicle classification and detection are highly complex which provides coarse-grained out comes because of underfitting or overfitting of the model. Due to advanced accomplishments of the Deep Learning, it was efficiently implemented to image classification and detection of objects. This proposed paper come up with a new approach which makes use of convolutional neural networks concept in Deep Learning. It consists of two steps: i) vehicle classification ii) vehicle license plate recognition. Numerous classic modules of neural networks had been implemented in training and testing the vehicle classification and detection of license plate model, such as CNN (convolutional neural networks), TensorFlow, and Tesseract-OCR. The suggested technique can determine the vehicle type, number plate and other alternative data effectively. This model provides security and log details regarding vehicles by using AI Surveillance. It guides the surveillance operators and assists human resources. With the help of the original dataset (training) and enriched dataset (testing), this customized model(algorithm) can achieve best out come with a standard accuracy of around 97.32% in classification and detection of vehicles. By enlarging the quantity of the training dataset, the loss function and mislearning rate declines progressively. Therefore, this proposed model which uses Deep Learning had better performance and flexibility. When compared to out standing techniques in the strategic Image datasets, this deep learning mode lscan gethigher competitor outcomes. Eventually, the proposed system suggests modern methods for advance ment of the customized model and forecasts the progressive growth of deep learning performance in the exploration of artificial intelligence (AI) &machine learning (ML) techniques. 
Keywords: Convolutional neural network, Vehicle classification, Vehicle License plate Recognition, Deep Learning.
Scope of the Article: Classification