Efficient Vehicle Detection and Distance Estimation Based on Aggregated Channel Features and Inverse Perspective Mapping from a Single Camera

Kim, Jong Bae

doi:10.3390/sym11101205

Cited by 23 publications

(14 citation statements)

References 40 publications

(65 reference statements)

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“…The average accuracy of the resulting vehicle detectors was approximately 95%. Figure 13 shows the results of applying Aggregated Channel Features (ACF) [24,25], Fast R-CNN [26,27], Single Shot Detector (SSD) [28,29], feature information-based vehicle detectors, and the proposed method to detect vehicles in various terminal environments. The proposed method provides good results for the detection of vehicles in a tunnel environment.…”

Section: Resultsmentioning

confidence: 99%

Vehicle Detection Using Deep Learning Technique in Tunnel Road Environments

Kim

2020

Symmetry

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This paper proposes a real-time detection method for a car driving ahead in real time on a tunnel road. Unlike the general road environment, the tunnel environment is irregular and has significantly lower illumination, including tunnel lighting and light reflected from driving vehicles. The environmental restrictions are large owing to pollution by vehicle exhaust gas. In the proposed method, a real-time detection method is used for vehicles in tunnel images learned in advance using deep learning techniques. To detect the vehicle region in the tunnel environment, brightness smoothing and noise removal processes are carried out. The vehicle region is learned after generating a learning image using the ground-truth method. The YOLO v2 model, with an optimal performance compared to the performances of deep learning algorithms, is applied. The training parameters are refined through experiments. The vehicle detection rate is approximately 87%, while the detection accuracy is approximately 94% for the proposed method applied to various tunnel road environments.

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Section: Resultsmentioning

confidence: 99%

Vehicle Detection Using Deep Learning Technique in Tunnel Road Environments

Kim

2020

Symmetry

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“…These classifiers usually include support vector machine, random forest, neural network, etc. However, due to different shooting angles of traffic block the port, complex background and lighting, and other factors, traditional features often fail to meet the requirements, resulting in low classification accuracy [10,11].…”

Section: Design Of Image Classifiermentioning

confidence: 99%

“…We have split the total 500 samples into 400 training samples and 100 testing samples, in which the training accuracy rate reached 99.3% after 1000 iterations of the training dataset and the testing accuracy rate reached 98.2% after 10 iterations of the testing dataset. factors, traditional features often fail to meet the requirements, resulting in low classification accuracy [10,11]. Since 2012, the convolutional neural network has shown a strong advantage in image classification and its stronger feature presentation ability has also been recognized by more people [12].…”

Section: Design Of Image Classifiermentioning

confidence: 99%

Research on Image Adaptive Enhancement Algorithm under Low Light in License Plate Recognition System

Shi

Yuan

2020

Symmetry

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The traffic block port monitors and manages the road traffic by shooting and recording the motor vehicles. However, due to the complex factors such as shooting angle, light condition, environmental background, etc., the recognition rate of license plate is not high enough. High light and low light under complex lighting conditions are symmetry problems. This paper analyzes and solves the low light problem in detail, an image adaptive enhancement algorithm under low light conditions is proposed in the paper. The algorithm mainly includes four modules, among which, the fast image classification module uses the deep and separable convolutional neural network to classify low-light images into low-light images by day and low-light images by night, greatly reducing the computation burden on the basis of ensuring the classification accuracy. The image enhancement module inputs the classified images into two different image enhancement algorithms and adopts the idea of dividing and ruling; the image quality evaluation module adopts a weighted comprehensive evaluation index. The final experiment shows that the comprehensive evaluation indexes are all greater than 0.83, which can improve the subsequent recognition of vehicle face and license plate.

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“…A method of detecting an object and a method of estimating a vehicle's distance from a bird's eye view through inverse perspective mapping (IPM) were applied. By applying IPM and transforming a 2D input image into 3D by generating an image projected in three dimensions, the distance between the detected vehicle and the autonomous vehicle was detected 21) . Usually the safety procedure was conducted by displaying a safety sign board and blocking with safety cones while conducting the maintenance at the highway emergency lanes.…”

Section: Introductionmentioning

confidence: 99%

Safety Working Environment at Highway: Safety Warning Detector (SWAD) System

Hassan¹,

Yusof²,

Ikhsan³

et al. 2021

Evergreen

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The research study was carried out for the safety of highway maintenance workers in order to avoid incidents or hazards in the direct emergency lane on the highway through the implementation of a danger sensor device which known as Signal Warning Detector (SWAD). The distance sensor detected the object at a certain distance and sent the signal to the output via the siren device and emergency light. The device sensor was mounted on the highway worker vehicle. It also sent signal to the receiver to alert employee by giving early signal of danger detected. The receiver alerted employee by vibrating the vibrator motor that was attached at highway worker's arm. The main objective of the SWAD was to increase the safety procedure and precaution for maintenance highway workers that currently doing their job at the emergency lane of the highway. The Signal Warning Detector (SWAD) had successfully implemented by detecting the vehicle or danger from behind in 70 meter maximum distance at straight emergency lane. The SWAD was effective to be used on highway worker vehicles for additional safety by giving early danger signal.

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Efficient Vehicle Detection and Distance Estimation Based on Aggregated Channel Features and Inverse Perspective Mapping from a Single Camera

Cited by 23 publications

References 40 publications

Vehicle Detection Using Deep Learning Technique in Tunnel Road Environments

Vehicle Detection Using Deep Learning Technique in Tunnel Road Environments

Research on Image Adaptive Enhancement Algorithm under Low Light in License Plate Recognition System

Safety Working Environment at Highway: Safety Warning Detector (SWAD) System

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