A Study of Self-Adaptive Front Vehicle Recognition Algorithm Based on Multi-Features

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.945-949.1815 ◽

2014 ◽

Vol 945-949 ◽

pp. 1815-1819

Author(s):

Mei Hua Xu ◽

Chen Jun Xia ◽

Huai Meng Zheng

Keyword(s):

Vehicle Detection ◽

Detection Algorithm ◽

Recognition Algorithm ◽

License Plate ◽

Improved Method ◽

Vehicle Recognition ◽

Potential Risks ◽

Self Adaptive

With the development of intelligent driving technology, recognition the vehicle in front of our cars became the hotspot in the field of intelligent driving research. This paper presents a self-adaptive front vehicle recognition algorithm with some unique improved method on the basis of analyzing and comparing the popular vehicle detection algorithm of domestic and foreign. Using the gray feature, vehicle shadow feature, taillights feature, license plate color domain feature and other features, the recognition algorithm can detect the vehicle in front of cars effectively, find out the safe passage area and avoid the potential risks. Finally, the feasibility of the algorithm is verified by experiment results with MATLAB tools.

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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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License plate identification and recognition in a non-standard environment using neural pattern matching

Complex & Intelligent Systems ◽

10.1007/s40747-021-00419-5 ◽

2021 ◽

Author(s):

Imran Shafi ◽

Imtiaz Hussain ◽

Jamil Ahmad ◽

Pyoung Won Kim ◽

Gyu Sang Choi ◽

...

Keyword(s):

Character Recognition ◽

Detection Algorithm ◽

Input Image ◽

Recognition Algorithm ◽

Detection Accuracy ◽

License Plate ◽

Recognition Mechanism ◽

Robust Detection ◽

Feature Representations ◽

Recognition Efficiency

AbstractNon-standard license plates are a part of current traffic trends in Pakistan. Private number plates should be recognized and, monitored for several purposes including security as well as a well-developed traffic system. There is a challenging task for the authorities to recognize and trace the locations for the certain number plate vehicle. In a developing country like Pakistan, it is tough to have higher constraints on the efficiency of any license plate identification and recognition algorithm. Character recognition efficiency should be a route map for the achievement of the desired results within the specified constraints. The main goal of this study is to devise a robust detection and recognition mechanism for non-standard, transitional vehicle license plates generally found in developing countries. Improvement in the character recognition efficiency of drawn and printed plates in different styles and fonts using single using multiple state-of-the-art technologies including machine-learning (ML) models. For the mentioned study, 53-layer deep convolutional neural network (CNN) architecture based on the latest variant of object detection algorithm-You Only Look Once (YOLOv3) is employed. The proposed approach can learn the rich feature representations from the data of diversified license plates. The input image is first pre-processed for quality improvement, followed by dividing it into suitable-sized grid cells to find the correct location of the license plate. For training the CNN, license plate characters are segmented. Lastly, the results are post-processed and the accuracy of the proposed model is determined through standard benchmarks. The proposed method is successfully tested on a large image dataset consisting of eight different types of license plates from different provinces in Pakistan. The proposed system is expected to play an important role in implementing vehicle tracking, payment for parking fees, detection of vehicle over-speed limits, reducing road accidents, and identification of unauthorized vehicles. The outcome shows that the proposed approach achieves a plate detection accuracy of 97.82% and the character recognition accuracy of 96%.

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