Part-Aware Refinement Network for Occlusion Vehicle Detection

Wang, Qifan; Xu, Ning; Huang, Baojin; Wang, Guangcheng

doi:10.3390/electronics11091375

Cited by 4 publications

(3 citation statements)

References 37 publications

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“…Amrouche and his colleagues proposed a Yolov4 architecture for a real-time vehicle detection and tracking system [ 113 ]. Wang et al [ 114 ] introduced an integrated part-aware refinement network, which combines multi-scale training and component confidence generation strategies in vehicle detection. This system improves detection accuracy and time taken in detecting various vehicles on publicly available datasets.…”

Section: Application Of Dcnn For Vehicle Detection and Classificationmentioning

confidence: 99%

Deep Learning Techniques for Vehicle Detection and Classification from Images/Videos: A Survey

Berwo

Khan

Fang

et al. 2023

Sensors

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Detecting and classifying vehicles as objects from images and videos is challenging in appearance-based representation, yet plays a significant role in the substantial real-time applications of Intelligent Transportation Systems (ITSs). The rapid development of Deep Learning (DL) has resulted in the computer-vision community demanding efficient, robust, and outstanding services to be built in various fields. This paper covers a wide range of vehicle detection and classification approaches and the application of these in estimating traffic density, real-time targets, toll management and other areas using DL architectures. Moreover, the paper also presents a detailed analysis of DL techniques, benchmark datasets, and preliminaries. A survey of some vital detection and classification applications, namely, vehicle detection and classification and performance, is conducted, with a detailed investigation of the challenges faced. The paper also addresses the promising technological advancements of the last few years.

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Section: Application Of Dcnn For Vehicle Detection and Classificationmentioning

confidence: 99%

Deep Learning Techniques for Vehicle Detection and Classification from Images/Videos: A Survey

Berwo

Khan

Fang

et al. 2023

Sensors

View full text Add to dashboard Cite

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“…Efficient vehicle identification and concentration estimation play a significant role in facilitating transportation. Identification and tracking are the two main steps required here—the precise recognition of cars amid other things in the scene [ 8 , 9 , 10 ]. Additionally, the cars presented may be of varying sizes and shapes.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Detection and Recognition Approach in Multi-Scale Traffic Monitoring System via Graph-Based Data Optimization

Wieczorek

Tahir

Akhter

et al. 2023

Sensors

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Over the past few years, significant investments in smart traffic monitoring systems have been made. The most important step in machine learning is detecting and recognizing objects relative to vehicles. Due to variations in vision and different lighting conditions, the recognition and tracking of vehicles under varying extreme conditions has become one of the most challenging tasks. To deal with this, our proposed system presents an adaptive method for robustly recognizing several existing automobiles in dense traffic settings. Additionally, this research presents a broad framework for effective on-road vehicle recognition and detection. Furthermore, the proposed system focuses on challenges typically noticed in analyzing traffic scenes captured by in-vehicle cameras, such as consistent extraction of features. First, we performed frame conversion, background subtraction, and object shape optimization as preprocessing steps. Next, two important features (energy and deep optical flow) were extracted. The incorporation of energy and dense optical flow features in distance-adaptive window areas and subsequent processing over the fused features resulted in a greater capacity for discrimination. Next, a graph-mining-based approach was applied to select optimal features. Finally, the artificial neural network was adopted for detection and classification. The experimental results show significant performance in two benchmark datasets, including the LISA and KITTI 7 databases. The LISA dataset achieved a mean recognition rate of 93.75% on the LDB1 and LDB2 databases, whereas KITTI attained 82.85% accuracy on separate training of ANN.

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“…In recent years, the degree of automation of intelligent vehicles has gradually increased [1,2]. Intelligent vehicles can primarily reduce the influence of human factors and decrease the occurrence of traffic accidents [3].…”

Section: Introductionmentioning

confidence: 99%

Collaborative Accurate Vehicle Positioning Based on Global Navigation Satellite System and Vehicle Network Communication

et al. 2022

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Intelligence is a direction of development for vehicles and transportation. Accurate vehicle positioning plays a vital role in intelligent driving and transportation. In the case of obstruction or too few satellites, the positioning capability of the Global navigation satellite system (GNSS) will be significantly reduced. To eliminate the effect of unlocalization due to missing GNSS signals, a collaborative multi-vehicle localization scheme based on GNSS and vehicle networks is proposed. The vehicle first estimates the location based on GNSS positioning information and then shares this information with the environmental vehicles through vehicle network communication. The vehicle further integrates the relative position of the ambient vehicle observed by the radar with the ambient vehicle position information obtained by communication. A smaller error estimate of the position of self-vehicle and environmental vehicles is obtained by correcting the positioning of self-vehicle and environmental vehicles. The proposed method is validated by simulating multi-vehicle motion scenarios in both lane change and straight-ahead scenarios. The root-mean-square error of the co-location method is below 0.5 m. The results demonstrate that the combined vehicle network communication approach has higher accuracy than single GNSS positioning in both scenarios.

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Part-Aware Refinement Network for Occlusion Vehicle Detection

Cited by 4 publications

References 37 publications

Deep Learning Techniques for Vehicle Detection and Classification from Images/Videos: A Survey

Deep Learning Techniques for Vehicle Detection and Classification from Images/Videos: A Survey

Vehicle Detection and Recognition Approach in Multi-Scale Traffic Monitoring System via Graph-Based Data Optimization

Collaborative Accurate Vehicle Positioning Based on Global Navigation Satellite System and Vehicle Network Communication

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