Vehicle detection in intelligent transport system under a hazy environment: a survey

Husain, Agha Asim; Maity, Tanmoy; Yadav, Ravindra Kumar

doi:10.1049/iet-ipr.2018.5351

Cited by 40 publications

(16 citation statements)

References 85 publications

(89 reference statements)

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“…The two-stage object detection can be considered a coarse-to-fine process, while the one-stage object detection can be considered a completein-one-step procedure. Earlier studies [23,[44][45][46] show analysis of deep learning-based object detection models ignoring several other essential aspects in comparison. Table 2 lists the observed strengths and limitations of two-stage and one-stage detection models along with the significant innovations the model proposes in the field of deep learning-based object detection.…”

Section: Summary Of Two-stage/one-stage Object Detections Modelsmentioning

confidence: 99%

RETRACTED: Road Object Detection: A Comparative Study of Deep Learning-Based Algorithms

Haris

Głowacz

2021

Electronics

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Automated driving and vehicle safety systems need object detection. It is important that object detection be accurate overall and robust to weather and environmental conditions and run in real-time. As a consequence of this approach, they require image processing algorithms to inspect the contents of images. This article compares the accuracy of five major image processing algorithms: Region-based Fully Convolutional Network (R-FCN), Mask Region-based Convolutional Neural Networks (Mask R-CNN), Single Shot Multi-Box Detector (SSD), RetinaNet, and You Only Look Once v4 (YOLOv4). In this comparative analysis, we used a large-scale Berkeley Deep Drive (BDD100K) dataset. Their strengths and limitations are analyzed based on parameters such as accuracy (with/without occlusion and truncation), computation time, precision-recall curve. The comparison is given in this article helpful in understanding the pros and cons of standard deep learning-based algorithms while operating under real-time deployment restrictions. We conclude that the YOLOv4 outperforms accurately in detecting difficult road target objects under complex road scenarios and weather conditions in an identical testing environment.

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Section: Summary Of Two-stage/one-stage Object Detections Modelsmentioning

confidence: 99%

RETRACTED: Road Object Detection: A Comparative Study of Deep Learning-Based Algorithms

Haris

Głowacz

2021

Electronics

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“…In recent years, researchers began to apply various technical means to increase the detection accuracy of anomaly objects for X-ray images in practical applications. However, the anomaly object detection for X-ray images enfaces great challenges, due to the following reasons: (1) the objects in the luggage are often placed at any angle; (2) the multiple objects are overlapped; (3) the variations of the object scales are dramatic.…”

Section: Introductionmentioning

confidence: 99%

“…This article presents an anomaly object detection method for X-ray images with multiple convolutions and bigger discriminative region of interest (RoI) pooling. The main contributions of this study are as follows: (1) According to the fact that the object edge in the X-ray images is obvious, but the texture is scarce, ResNeXt-50 is used as the backbone network and a learnable Gabor convolution layer is introduced into the lower layer to capture the edge of the object. In addition, deformable convolution is incorporated into the middle layer to solve the multi-scale deformation problem of the objects.…”

Section: Introductionmentioning

confidence: 99%

EAOD‐Net: Effective anomaly object detection networks for X‐ray images

et al. 2022

IET Image Processing

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Anomaly object detection is the core technology in the application for X‐ray images. However, the accuracy of current X‐ray anomaly object detection method still needs to be improved. In this paper, an effective anomaly object detection network is proposed to improve the detection accuracy of anomaly object for X‐ray images. Firstly, learnable Gabor convolution layer, deformable convolution, and spatial attention mechanism are introduced to enhance the representative ability of features in ResNeXt. Then, dense local regression is applied to predict the offset of multiple dense boxes in region proposal to locate the object accurately. At last, bigger discriminative RoI pooling is proposed to classify the candidate boxes to improve the accuracy of object classification. Experimental results on the SIXray and OPIXray datasets show that compared with the state‐of‐the‐art methods, the proposed EAOD‐Net can achieve the competitive detection performance.

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“…It has shown an interesting increase in research efforts due to numerous applications such as 3D navigation, obstacle detection and 3D reconstruction. One of the main applications of the stereo vision methods is advanced driver assistance systems (ADAS) [1][2][3], which aims at improving driving comfort and safety of people and goods in the road. Some works propose to solve the problem of matching stereo images using local algorithms based on block matching or pixel correlation due to their simplicity [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Dense spatio‐temporal stereo matching for intelligent driving systems

Kerkaou

Ansari

Masmoudi

et al. 2021

IET Image Processing

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This paper addresses the problem of matching stereo images acquired by a stereo system mounted aboard an intelligent vehicle. The main idea behind the new method consists in involving temporal matching between a current stereo pair and its preceding one to achieve the spatial matching of the former stereo by involving the matching results obtained at the last frame. The proposed method is achieved in three main steps. First, an edge based disparity map is derived from the disparity map of the preceding frame, we call it the assisting disparity map (ADM). Second, for each scan‐line, a set of local ranges and global ranges are deduced from the ADM to keep only potential matching candidates. Third, the matching is done on the basis of dynamic programming algorithm by involving in the resulting local and global ranges we get from the last step. The proposed approach has been tested on both real and synthetic stereo sequences and the results demonstrate its effectiveness.

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Vehicle detection in intelligent transport system under a hazy environment: a survey

Cited by 40 publications

References 85 publications

RETRACTED: Road Object Detection: A Comparative Study of Deep Learning-Based Algorithms

RETRACTED: Road Object Detection: A Comparative Study of Deep Learning-Based Algorithms

EAOD‐Net: Effective anomaly object detection networks for X‐ray images

Dense spatio‐temporal stereo matching for intelligent driving systems

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