Vehicle Detection and Classification Using Convolutional Neural Networks

Sheng, Minglan; Zhang, Qi; Lou, Lu; Zheng, Yu

doi:10.1109/ddcls.2018.8516099

Cited by 15 publications

(3 citation statements)

References 5 publications

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“…rough the study evaluation, the authors recognize that the proposed model gives suitable detection rate; however, it gives high false detection rate especially in low training dataset [10]. Sheng et al provide the concept of using R-CNN model to increase the dataset of traffic detection datasets [11]. Author evaluates the model in detecting vehicles based on different angels and multiple scenes [8].…”

Section: Related Workmentioning

confidence: 99%

Vehicle Detection for Vision-Based Intelligent Transportation Systems Using Convolutional Neural Network Algorithm

Khalifa

Wajdi

Saeed

et al. 2022

Journal of Advanced Transportation

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Vehicle detection in Intelligent Transportation Systems (ITS) is a key factor ensuring road safety, as it is necessary for the monitoring of vehicle flow, illegal vehicle type detection, incident detection, and vehicle speed estimation. Despite the growing popularity in research, it remains a challenging problem that must be solved. Hardware-based solutions such as radars and LIDAR are been proposed but are too expensive to be maintained and produce little valuable information to human operators at traffic monitoring systems. Software based solutions using traditional algorithms such as Histogram of Gradients (HOG) and Gaussian Mixed Model (GMM) are computationally slow and not suitable for real-time traffic detection. Therefore, the paper will review and evaluate different vehicle detection methods. In addition, a method of utilizing Convolutional Neural Network (CNN) is used for the detection of vehicles from roadway camera outputs to apply video processing techniques and extract the desired information. Specifically, the paper utilized the YOLOv5s architecture coupled with k-means algorithm to perform anchor box optimization under different illumination levels. Results from the simulated and evaluated algorithm showed that the proposed model was able to achieve a mAP of 97.8 in the daytime dataset and 95.1 in the nighttime dataset.

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Section: Related Workmentioning

confidence: 99%

Vehicle Detection for Vision-Based Intelligent Transportation Systems Using Convolutional Neural Network Algorithm

Khalifa

Wajdi

Saeed

et al. 2022

Journal of Advanced Transportation

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“…Deep residual learning framework improves detection and accuracy of a convolutional neural network [18] and were implemented in this paper. Pre-trained residual learning networks with varying layers is available which can be used to perform custom detection [19]. Similarly, there are several other frameworks with varying architectures and performances.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep learning‐based vehicle occupancy detection in an open parking lot using thermal camera

Paidi

Fleyeh

Nyberg

2020

IET intell. transp. syst

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Parking vehicle is a daunting task and a common problem in many cities around the globe. The search for parking space leads to congestion, frustration and increased air pollution. Information of a vacant parking space would facilitate to reduce congestion and subsequent air pollution. Therefore, aim of the paper is to acquire vehicle occupancy in an open parking lot using deep learning. Thermal camera was used to collect the data during varying environmental conditions such as; sunny, dusk, dawn, dark and snowy conditions. Vehicle detection with deep learning was implemented where image classification and object localization were performed for multi object detection. The dataset consists of 527 images which were manually labelled as there were no pre-labelled thermal images available. Multiple deep learning networks such as Yolo, ReNet18, ResNet50 and GoogleNet with varying layers and architectures were evaluated on vehicle detection. Yolo, GoogleNet and ResNet18 are computationally efficient detectors which took less processing time while Resnet50 produced better detection results compared to other detectors. However, ResNet18 also produced minimal miss rates and is suitable for real time vehicle detection. The detected results were compared with a template of parking spaces and IoU value is used to identify vehicle occupancy information.

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“…The study showed that the performance boost provided by CNN-based vehicle detector over a standard geometric approach. Sheng and et al [7] proposed vehicle detection system using neural networks. They presented a method based on CNN.…”

Section: Literature Reviewmentioning

confidence: 99%

Vehicle Brand Detection Using Deep Learning Algorithms

Kunduraci¹,

Kahramanlı²

2019

International Journal of Applied Mathematics Electronics and Computers

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The aim of this study is to provide a solution for different applications using an effective and cost-effective method to detect the brand and model of vehicles. A classification method is implemented using deep neural network in the detection of the vehicle brand. We aimed to detect 20 vehicle brands which are covers approximately 96.91% of cars registered in traffic in Turkey. The proposed solution is tested on 1280 various images taken from different angles and obtained from different sources. Faster-RCNN method which is one of deep neural networks is used to brand detection of vehicles in this study. It is observed that Faster-RCNN method performs 67.66% classification accuracy.

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Vehicle Detection and Classification Using Convolutional Neural Networks

Cited by 15 publications

References 5 publications

Vehicle Detection for Vision-Based Intelligent Transportation Systems Using Convolutional Neural Network Algorithm

Vehicle Detection for Vision-Based Intelligent Transportation Systems Using Convolutional Neural Network Algorithm

Deep learning‐based vehicle occupancy detection in an open parking lot using thermal camera

Vehicle Brand Detection Using Deep Learning Algorithms

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