scholarly journals Fast and Accurate Deep Learning Architecture on Vehicle Type Recognition

10.55003/001 ◽  
2021 ◽  
Author(s):  
Olarik Surinta ◽  
Narong Boonsirisumpun
Keyword(s):  
Deep Learning ◽  
Vehicle Type

scholarly journals Fast and Accurate Deep Learning Architecture on Vehicle Type Recognition

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Olarik Surinta ◽  
Narong Boonsirisumpun
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Image Data ◽  
Machine Learning Techniques ◽  
License Plate ◽  
Accuracy Rate ◽  
Vehicle Type ◽  
Vehicle Data ◽  
Learning Techniques ◽  
Learning Technique

Vehicle Type Recognition has a significant problem that happens when people need to search for vehicle data from a video surveillance system at a time when a license plate does not appear in the image. This paper proposes to solve this problem with a deep learning technique called Convolutional Neural Network (CNN), which is one of the latest advanced machine learning techniques. In the experiments, researchers collected two datasets of Vehicle Type Image Data (VTID I & II), which contained 1,310 and 4,356 images, respectively. The first experiment was performed with 5 CNN architectures (MobileNets, VGG16, VGG19, Inception V3, and Inception V4), and the second experiment with another 5 CNNs (MobileNetV2, ResNet50, Inception ResNet V2, Darknet-19, and Darknet-53) including several data augmentation methods. The results showed that MobileNets, when combine with the brightness augmented method, significantly outperformed other CNN architectures, producing the highest accuracy rate at 95.46%. It was also the fastest model when compared to other CNN networks.

scholarly journals Trajectory Object Detection using Deep Learning Algorithms

2019 ◽  
Vol 8 (3) ◽  
pp. 7895-7898
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Large Scale ◽  
Smart Cities ◽  
Learning Algorithms ◽  
Big Data Analytics ◽  
Light Condition ◽  
Transport Systems ◽  
Video Footage ◽  
Vehicle Type

Video surveillance data in smart cities needs to analyze a large amount of video footage in order to locate the people who are violating the traffic rules. The fact is that it is very easy for the human being to recognize different objects in images and videos. For a computer program this is quite a difficult task. Hence there is a need for visual big data analytics which involves processing and analyzing large scale visual data such as images or videos. One major application of trajectory object detection is the Intelligent Transport Systems (ITS). Vehicle type detection, tracking and classification play an important role in ITS. In order to analyze huge amount of video footage deep learning algorithms have been deployed. The main phase of vehicle type detection includes annotating the data, training the model and validating the model. The problems and challenges in identifying or detecting type of vehicle are due to weather, shadows, blurring effect, light condition and quality of the data. In this paper deep learning algorithms such as Faster R CNN and Mask R CNN and Frameworks like YOLO were used for the object detection. Dataset (different types of vehicle pictures in video format) were collected both from in-house premises as well as from the Internet to detect and recognize the type of vehicles which are common in traffic systems. The experimental results show that among the three approaches used the Mask R CNN algorithm is found to be more efficient and accurate in vehicle type detection.

scholarly journals Vehicle type detection based on deep learning in traffic scene

2018 ◽  
Vol 131 ◽  
pp. 564-572 ◽  
Author(s):  
Li Suhao ◽  
Lin Jinzhao ◽  
Li Guoquan ◽  
Bai Tong ◽  
Wang Huiqian ◽  
...  
Keyword(s):  
Deep Learning ◽  
Vehicle Type

A Novel Approach for Vehicle Type Classification and Speed Prediction Using Deep Learning

2020 ◽  
Vol 17 (5) ◽  
pp. 2237-2242
Author(s):  
E. S. Madhan ◽  
S. Neelakandan ◽  
R. Annamalai
Keyword(s):  
Deep Learning ◽  
Intelligent Transportation System ◽  
Detection System ◽  
Learning Technologies ◽  
Training Dataset ◽  
Automation System ◽  
Vehicle Type ◽  
Novel Approach ◽  
Vehicle Type Classification ◽  
Type Classification

In Vehicles automation system, Classification and speed detection has become an important research challenge in road safety and intelligent transportation system. Many systems like pattern recognition, image processing and machine learning technologies have overcome numerous hindrances to accomplish this goal. In this paper, we demonstrate a speed detection system and vehicle type classification founded on deep learning technique. Moreover, we built up Modular Neural Network (MNN) architecture, advancement algorithm and its parameters are acquired by training dataset. This integrated part of a system will enhance to finding in automation detection and traffic flow management system.

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