Convolutional Neural Network Based Vehicle Classification in Adverse Illuminous Conditions for Intelligent Transportation Systems

Butt, Muhammad Arif; Khattak, Asad Masood; Shafique, Sarmad; Hayat, Bashir; Abid, Saima; Kim, Ki-Il; Ayub, Muhammad; Sajid, Ahthasham; Adnan, Awais

doi:10.1155/2021/6644861

Cited by 40 publications

(15 citation statements)

References 41 publications

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“…The above discussion clearly demonstrates the rise in the usage of cameras and related computer vision algorithms in cars and car safety [25,26]. The groundbreaking development of neural networks during the last decade has also contributed to their application in image processing [27], where one of their most important functions is the detection of specific objects in images [28].…”

Section: St-openunisthrmentioning

confidence: 95%

System for automatic detection and classification of cars in traffic

Bralić

Musić

2022

St open

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Objective: To develop a system for automatic detection and classification of cars in traffic in the form of a device for autonomic, real-time car detection, license plate recognition, and car color, model, and make identification from video.Methods: Cars were detected using the You Only Look Once (YOLO) v4 detector. The YOLO output was then used for classification in the next step. Colors were classified using the k-Nearest Neighbors (kNN) algorithm, whereas car models and makes were identified with a single-shot detector (SSD). Finally, license plates were detected using the OpenCV library and Tesseract-based optical character recognition. For the sake of simplicity and speed, the subsystems were run on an embedded Raspberry Pi computer.Results: A camera was mounted on the inside of the windshield to monitor cars in front of the camera. The system processed the camera’s video feed and provided information on the color, license plate, make, and model of the observed car. Knowing the license plate number provides access to details about the car owner, roadworthiness, car or license place reports missing, as well as whether the license plate matches the car. Car details were saved to file and displayed on the screen. The system was tested on real-time images and videos. The accuracies of car detection and car model classification (using 8 classes) in images were 88.5% and 78.5%, respectively. The accuracies of color detection and full license plate recognition were 71.5% and 51.5%, respectively. The system operated at 1 frame per second (1 fps).Conclusion: These results show that running standard machine learning algorithms on low-cost hardware may enable the automatic detection and classification of cars in traffic. However, there is significant room for improvement, primarily in license plate recognition. Accordingly, potential improvements in the future development of the system are proposed.

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Section: St-openunisthrmentioning

confidence: 95%

System for automatic detection and classification of cars in traffic

Bralić

Musić

2022

St open

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“…Butt et al [14] suggested a CNN-related vehicle classification algorithm to improve the strength of vehicle classification in real-time applications. Previously, pre-trained Inception-v3, AlexNet, Visual Geometry Group (VGG), ResNet, and GoogleNet were fine-tuned over self-constructed vehicle datasets so as to evaluate its execution efficiency in terms of convergence and accuracy.…”

Section: Related Workmentioning

confidence: 99%

Lightning Search Algorithm with Deep Transfer Learning-Based Vehicle Classification

Alnfiai¹

2023

Computers, Materials &Amp; Continua

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There is a drastic increase experienced in the production of vehicles in recent years across the globe. In this scenario, vehicle classification system plays a vital part in designing Intelligent Transportation Systems (ITS) for automatic highway toll collection, autonomous driving, and traffic management. Recently, computer vision and pattern recognition models are useful in designing effective vehicle classification systems. But these models are trained using a small number of hand-engineered features derived from small datasets. So, such models cannot be applied for real-time road traffic conditions. Recent developments in Deep Learning (DL)-enabled vehicle classification models are highly helpful in resolving the issues that exist in traditional models. In this background, the current study develops a Lightning Search Algorithm with Deep Transfer Learning-based Vehicle Classification Model for ITS, named LSADTL-VCITS model. The key objective of the presented LSADTL-VCITS model is to automatically detect and classify the types of vehicles. To accomplish this, the presented LSADTL-VCITS model initially employs You Only Look Once (YOLO)-v5 object detector with Capsule Network (CapsNet) as baseline model. In addition, the proposed LSADTL-VCITS model applies LSA with Multilayer Perceptron (MLP) for detection and classification of the vehicles. The performance of the proposed LSADTL-VCITS model was experimentally validated using benchmark dataset and the outcomes were examined under several measures. The experimental outcomes established the superiority of the proposed LSADTL-VCITS model compared to existing approaches.

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“…However, the region of interest is usually strongly cropped to remove the background and requires a certain angle image; this strategy, for example, is suited for parking lot cameras. There have been successful experiments to determine the type of vehicle, such as a car, van, truck, or another, with an accuracy of up to 99.68 % using a modified pre-trained ResNet-152 model [3] or 76.28 % using ResNet34 [4]. Another example shows that the AlexNet architecture can be used to produce a perfect classifier to assess the position of a vehicle [16].…”

Section: B Classification Of Vehicle Attributesmentioning

confidence: 99%

“…vehicle is constantly being examined due to the increasing use and research of convolutional neural networks (CNNs) and the development of the graphics processing unit. CNN has already been used to detect and track cars or to count the number of cars passing by on the road [2], [3]. However, due to the most severe CNN constraint of requiring a large dataset, the transfer learning approach [4] was designed to tackle limited data resources.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Make Detection Using the Transfer Learning Approach

Komolovaitė

Kriščiūnas

Lagzdinyte-Budnike

et al. 2022

ELEKTRON ELEKTROTECH

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Vehicle detection and classification is an important part of an intelligent transportation surveillance system. Although car detection is a trivial task for deep learning models, studies have shown that when vehicles are visible from different angles, more research is relevant for brand classification. Furthermore, each year, more than 30 new car models are released to the United States market alone, implying that the model needs to be updated with new classes, and the task becomes more complex over time. As a result, a transfer learning approach has been investigated that allows the retraining of a model with a small amount of data. This study proposes an efficient solution to develop an updatable local vehicle brand monitoring system. The proposed framework includes the dataset preparation, object detection, and a view-independent make classification model that has been tested using two efficient deep learning architectures, EfficientNetV2 and MobileNetV2. The model was trained on the dominant car brands in Lithuania and achieved 81.39 % accuracy in classifying 19 classes, using 400 to 500 images per class.

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Convolutional Neural Network Based Vehicle Classification in Adverse Illuminous Conditions for Intelligent Transportation Systems

Cited by 40 publications

References 41 publications

System for automatic detection and classification of cars in traffic

System for automatic detection and classification of cars in traffic

Lightning Search Algorithm with Deep Transfer Learning-Based Vehicle Classification

Vehicle Make Detection Using the Transfer Learning Approach

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