Road Surface Status Classification Using Spectral Analysis of NIR Camera Images

Jönsson, Patrik; Casselgren, Johan; Thörnberg, Benny

doi:10.1109/jsen.2014.2364854

Cited by 87 publications

(53 citation statements)

References 21 publications

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“…Automatic classification is one of the main applications of spectral imaging [25,26]. In the first experience, the system has been used to analyze several kinds of white paper with the objective of developing a classification algorithm from the generated data (Scenario I).…”

Section: Resultsmentioning

confidence: 99%

Portable Multispectral System Based on Color Detector for the Analysis of Homogeneous Surfaces

2019

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In this work, a compact affordable and portable spectral imaging system is presented. The system is intended to be employed in general applications, such as material classification or determination of the concentration of chemical species together with colorimetric sensors. The imaging device is reduced to a small digital color detector with an active area of 3×2 mm2. This device provides a quantification of the incident emission in the form of four digital words corresponding to its averaged components blue, green, red, and near infrared. In this way, the size of the image is reduced to one pixel. The wavelength selection is carried out by means of a LED array disposed surrounding the color detector. The LEDs are selected to cover the wavelength range from 360 to 890 nm. A sequential measurement protocol is followed, and the generated data is transmitted to an external portable device via a Bluetooth link where a classification protocol is implemented in a custom-developed Android™ application. The presented system has been applied in three different scenarios involving material classification, meat freshness monitoring, and chemical analysis. The analysis of the data using principal components shows that it is possible to find a set of wavelengths where the classification of the samples is optimal.

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Section: Resultsmentioning

confidence: 99%

Portable Multispectral System Based on Color Detector for the Analysis of Homogeneous Surfaces

2019

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“…It has been proved that individual types of pavement can be separated from each other based on sensor readings. The results of the continuation of research (Jonsson et al 2015) were published in 2015. The goal set was to create a system that will be able to measure road conditions continuously without affecting its users.…”

Section: Methods Of Slippery Detectionmentioning

confidence: 99%

System for monitoring road slippery based on CCTV cameras and convolutional neural networks

Grabowski

Czyżewski

2020

J Intell Inf Syst

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The slipperiness of the surface is essential for road safety. The growing number of CCTV cameras opens the possibility of using them to automatically detect the slippery surface and inform road users about it. This paper presents a system of developed intelligent road signs, including a detector based on convolutional neural networks (CNNs) and the transfer-learning method employed to the processing of images acquired with video cameras. Based on photos taken in different light conditions by CCTV cameras located at the roadsides in Poland, four network topologies have been trained and tested: Resnet50 v2, Resnet152 v2, Vgg19, and Densenet201. The last-mentioned network has proved to give the best result with 98.34% accuracy of classification dry, wet, and snowy roads.

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“…In their study, the cost and number of devices used were very high and this created limitations to the applicability of their method. 16 Jian Li 17 has indicated that the prediction success is higher if the correct attributes are selected with the feature selection method in the SVM algorithm. The success of road surface prediction is between 80% and 90% in sensor-based optical systems, but for camera-based optical reflection (ICOR) systems it is between 70% and 80%.…”

Section: Related Workmentioning

confidence: 99%

The design and implementation of road condition warning system for drivers

Teke

Duran

2019

Measurement and Control

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Intelligent transportation systems are advanced applications that inform vehicle drivers about road conditions. The main purpose of the intelligent transportation systems is to reduce either tangible or intangible loss for the drivers by ensuring the safety of passengers and vehicles. In this study, a system is designed and implemented using wireless sensor networks to inform vehicle drivers about the condition of the road surface. Icing has been chosen as the primary focus of the study since it is considered to be a big threat to road and driver’s safety. The temperature at 10 cm depth of the road, air temperature, relative humidity, air pressure and conductivity values are used as the input data for the prediction of icing on the road surface. The data were previously collected on Raspberry Pi which is a single-board computer and the data were read and processed instantly via k-nearest neighbor algorithm. Using these collected data, the road surface condition is classified as icy, dry, wet or salty-wet. The analyzed results for the road surface condition are presented to the drivers via a mobile application in real time. The drivers are alerted visually and audibly as they approach the coordinates on the road where risky conditions are present.

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Road Surface Status Classification Using Spectral Analysis of NIR Camera Images

Cited by 87 publications

References 21 publications

Portable Multispectral System Based on Color Detector for the Analysis of Homogeneous Surfaces

Portable Multispectral System Based on Color Detector for the Analysis of Homogeneous Surfaces

System for monitoring road slippery based on CCTV cameras and convolutional neural networks

The design and implementation of road condition warning system for drivers

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