Efficient distinction of road surface conditions using surveillance camera images in night time

Horita, Yuukou; Kawai, Shohei; Furukane, Tatsuya; Shibata, Katsunari

doi:10.1109/icip.2012.6466902

Cited by 4 publications

(7 citation statements)

References 4 publications

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“…Compared to existing works to detect the road surface conditions at night, the deep learning models have advantage of high accuracy and can be applied to more scenes. In [22][23][24], data were collected from a certain road, and the validation was also tested for the same road. The feasibility of their models use in other scenes is not investigated, which is a limitation of their models.…”

Section: Discussionmentioning

confidence: 99%

“…Refs. [22,23] investigated the cases at night via images from cameras. Both papers use the Mahalanobis distance to distinguish the road conditions.…”

Section: Introductionmentioning

confidence: 99%

“…Compared to the references focused on the database during the day, fewer references investigated the road surface conditions at night. In addition, one of the limitations of [22][23][24] is that the databases that they collected are constrained to one given road, and even the test data are acquired on the same road, which certainly undermines the feasibility of the model to other scenes.…”

Section: Introductionmentioning

confidence: 99%

“…This system will be implemented on the autonomous vehicles in order to increase driving safety. In [22][23][24], the classification accuracy of models at night is lower compared with the accuracy of the models in the day. A possible reason is that the images captured at night depend greatly on the road illumination conditions.…”

Section: Introductionmentioning

confidence: 99%

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Application and Comparison of Deep Learning Methods to Detect Night-Time Road Surface Conditions for Autonomous Vehicles

et al. 2022

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Currently, road surface conditions ahead of autonomous vehicles are not well detected by the existing sensors on those autonomous vehicles. However, driving safety should be ensured for the weather-induced road conditions for day and night. An investigation into deep learning to recognize the road surface conditions in the day is conducted using the collected data from an embedded camera on the front of the vehicles. Deep learning models have only been proven to be successful in the day, but they have not been assessed for night conditions to date. The objective of this work is to propose deep learning models to detect on-line road surface conditions caused by weather ahead of the autonomous vehicles at night with a high accuracy. For this study, different deep learning models, namely traditional CNN, SqueezeNet, VGG, ResNet, and DenseNet models, are applied with performance comparison. Considering the current limitation of existing night-time detection, reflection features of different road surfaces are investigated in this paper. According to the features, night-time databases are collected with and without ambient illumination. These databases are collected from several public videos in order to make the selected models more applicable to more scenes. In addition, selected models are trained based on a collected database. Finally, in the validation, the accuracy of these models to classify dry, wet, and snowy road surface conditions at night can be up to 94%.

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Section: Discussionmentioning

confidence: 99%

“…Refs. [22,23] investigated the cases at night via images from cameras. Both papers use the Mahalanobis distance to distinguish the road conditions.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application and Comparison of Deep Learning Methods to Detect Night-Time Road Surface Conditions for Autonomous Vehicles

et al. 2022

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“…Related works can be found in the video processing domain, where wetness detection has been studied with two camera setups: (1) a surveillance camera at night, and (2) a camera onboard a vehicle. The detection of road surface wetness using surveillance camera images at night is relying on passing cars' headlights as a lighting source that creates a reflection artifact on the road area [14]. On-board video cameras use polarization changes of reflections on road surfaces or spatio-temporal reflection models [15]- [17].…”

Section: Related Workmentioning

confidence: 99%

Detecting road surface wetness from audio: A deep learning approach

Abdić

Fridman

Brown

et al. 2016

2016 23rd International Conference on Pattern Recognition (ICPR)

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We introduce a recurrent neural network architecture for automated road surface wetness detection from audio of tiresurface interaction. The robustness of our approach is evaluated on 785,826 bins of audio that span an extensive range of vehicle speeds, noises from the environment, road surface types, and pavement conditions including international roughness index (IRI) values from 25 in/mi to 1400 in/mi. The training and evaluation of the model are performed on different roads to minimize the impact of environmental and other external factors on the accuracy of the classification. We achieve an unweighted average recall (UAR) of 93.2 % across all vehicle speeds including 0 mph. The classifier still works at 0 mph because the discriminating signal is present in the sound of other vehicles driving by.

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A Smart Method to Distinguish Road Surface Conditions at Night-time using a Car-Mounted Camera

et al. 2014

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Efficient distinction of road surface conditions using surveillance camera images in night time

Cited by 4 publications

References 4 publications

Application and Comparison of Deep Learning Methods to Detect Night-Time Road Surface Conditions for Autonomous Vehicles

Application and Comparison of Deep Learning Methods to Detect Night-Time Road Surface Conditions for Autonomous Vehicles

Detecting road surface wetness from audio: A deep learning approach

A Smart Method to Distinguish Road Surface Conditions at Night-time using a Car-Mounted Camera

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