Application of Vehicle Detection Based On Deep Learning in Headlight Control

Vehicle detection and recognition require demanding advanced computational intelligence and resources in a real-time traffic surveillance system for effective traffic management of all possible contingencies. One of the focus areas of deep intelligent systems is to facilitate vehicle detection and recognition techniques for robust traffic management of heavy vehicles. The following are such sophisticated mechanisms: Support Vector Machine (SVM), Convolutional Neural Networks (CNN), Regional Convolutional Neural Networks (R-CNN), You Only Look Once (YOLO) model, etcetera. Accordingly, it is pivotal to choose the precise algorithm for vehicle detection and recognition, which also addresses the real-time environment. In this study, a comparison of deep learning algorithms, such as the Faster R-CNN, YOLOv2, YOLOv3, and YOLOv4, are focused on diverse aspects of the features. Two entities for transport heavy vehicles, the buses and trucks, constitute detection and recognition elements in this proposed work. The mechanics of data augmentation and transfer-learning is implemented in the model; to build, execute, train, and test for detection and recognition to avoid over-fitting and improve speed and accuracy. Extensive empirical evaluation is conducted on two standard datasets such as COCO and PASCAL VOC 2007. Finally, comparative results and analyses are presented based on real-time.

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Intelligent Highway Traffic Detection Algorithm based on Deep Learning

10.21203/rs.3.rs-805608/v1 ◽

2021 ◽

Author(s):

ming ji ◽

Chuanxia Sun ◽

Yinglei Hu

Keyword(s):

Deep Learning ◽

Real Time ◽

Traffic Congestion ◽

Road Network ◽

Vehicle Detection ◽

Detection Algorithm ◽

Recognition Algorithm ◽

Traffic Data ◽

Vehicle Recognition ◽

Environment Recognition

Abstract In order to solve the increasingly serious traffic congestion problem, an intelligent transportation system is widely used in dynamic traffic management, which effectively alleviates traffic congestion and improves road traffic efficiency. With the continuous development of traffic data acquisition technology, it is possible to obtain real-time traffic data in the road network in time. A large amount of traffic information provides a data guarantee for the analysis and prediction of road network traffic state. Based on the deep learning framework, this paper studies the vehicle recognition algorithm and road environment discrimination algorithm, which greatly improves the accuracy of highway vehicle recognition. Collect highway video surveillance images in different environments, establish a complete original database, build a deep learning model of environment discrimination, and train the classification model to realize real-time environment recognition of highway, as the basic condition of vehicle recognition and traffic event discrimination, and provide basic information for vehicle detection model selection. To improve the accuracy of road vehicle detection, the vehicle target labeling and sample preprocessing of different environment samples are carried out. On this basis, the vehicle recognition algorithm is studied, and the vehicle detection algorithm based on weather environment recognition and fast RCNN model is proposed. Then, the performance of the vehicle detection algorithm described in this paper is verified by comparing the detection accuracy differences between different environment dataset models and overall dataset models, different network structures and deep learning methods, and other methods.

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Vehicle Detection in Aerial Images Using Selective Search with a Simple Deep Learning Based Combination Classifier

Lecture Notes in Electrical Engineering - Proceedings of the Third International Conference on Microelectronics, Computing and Communication Systems ◽

10.1007/978-981-13-7091-5_21 ◽

2019 ◽

pp. 221-233 ◽

Cited By ~ 2

Author(s):

Tanuja Tewari ◽

Kaustubh V. Sakhare ◽

Vibha Vyas

Keyword(s):

Deep Learning ◽

Vehicle Detection ◽

Aerial Images ◽

Selective Search

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Lightweight PVIDNet: A Priority Vehicles Detection Network Model Based on Deep Learning for Intelligent Traffic Lights

Sensors ◽

10.3390/s20216218 ◽

2020 ◽

Vol 20 (21) ◽

pp. 6218

Author(s):

Rodrigo Carvalho Barbosa ◽

Muhammad Shoaib Ayub ◽

Renata Lopes Rosa ◽

Demóstenes Zegarra Rodríguez ◽

Lunchakorn Wuttisittikulkij

Keyword(s):

Deep Learning ◽

Traffic Control ◽

Detection System ◽

Lightweight Design ◽

Vehicle Detection ◽

Activation Function ◽

Urban Traffic ◽

Vehicle Classification ◽

Traffic Light ◽

Traffic Lights

Minimizing human intervention in engines, such as traffic lights, through automatic applications and sensors has been the focus of many studies. Thus, Deep Learning (DL) algorithms have been studied for traffic signs and vehicle identification in an urban traffic context. However, there is a lack of priority vehicle classification algorithms with high accuracy, fast processing, and a lightweight solution. For filling those gaps, a vehicle detection system is proposed, which is integrated with an intelligent traffic light. Thus, this work proposes (1) a novel vehicle detection model named Priority Vehicle Image Detection Network (PVIDNet), based on YOLOV3, (2) a lightweight design strategy for the PVIDNet model using an activation function to decrease the execution time of the proposed model, (3) a traffic control algorithm based on the Brazilian Traffic Code, and (4) a database containing Brazilian vehicle images. The effectiveness of the proposed solutions were evaluated using the Simulation of Urban MObility (SUMO) tool. Results show that PVIDNet reached an accuracy higher than 0.95, and the waiting time of priority vehicles was reduced by up to 50%, demonstrating the effectiveness of the proposed solution.

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