Real-time method for general road segmentation

Valente, Michelle; Stanciulescu, Bogdan

doi:10.1109/ivs.2017.7995758

Cited by 10 publications

(3 citation statements)

References 11 publications

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“…It's common to find in the literature studies focused on path detection that identify the path on different surfaces, but which don't extract relevant road features, such as surface type and also if there is some damage on that surface. ( [2], [3], [4], [5], [6] and several other examples as shown in this Systematic Literature Review about road detection [7]).…”

Section: Introductionmentioning

confidence: 84%

Road Surface Classification with Images Captured From Low-cost Camera - Road Traversing Knowledge (RTK) Dataset

Rateke

Justen

Wangenheim

2019

RITA

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The type of road pavement directly influences the way vehicles are driven. It’s common to find papers that deal with path detection but don’t take into account major changes in road surface patterns. The quality of the road surface has a direct impact on the comfort and especially on the safety of road users. In emerging countries it’s common to find unpaved roads or roads with no maintenance. Unpaved or damaged roads also impact in higher fuel costs and vehicle maintenance. This kind of analysis can be useful for both road maintenance departments as well as for autonomous vehicle navigation systems to verify potential critical points. For the experiments accomplishment upon the surface types and quality classification, we present a new dataset, collected with a low-cost camera. This dataset has examples of good and bad asphalt (with potholes and other damages) other types of pavement and also many examples of unpaved roads (with and without potholes). We also provide several frames from our dataset manually sorted in surface types for tests accuracy verification. Our road type and quality classifier was done through a simple Convolutional Neural Network with few steps and presents promising results in different datasets.

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Section: Introductionmentioning

confidence: 84%

Road Surface Classification with Images Captured From Low-cost Camera - Road Traversing Knowledge (RTK) Dataset

Rateke

Justen

Wangenheim

2019

RITA

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“…In this SLR, although we found several papers, we could not identify any work that dealt with road damage detection or road features other than painted road markings (Ardiyanto and Adji (2017), Jia et al (2017), Yuan et al (2015), Zu et al (2015) and Shi et al (2016)). Still in the road detection SLR, it is noticed that only 32.1% works did the detection in different surface types (eg: Li et al (2016), Wang et al (2016), Nguyen et al (2017) and Valente and Stanciulescu (2017)). Only 4 showed results with path detection working during the transition between surface types (Guo et al (2011), Guo et al (2012, Ososinski and Labrosse (2012) and Cristóforis et al (2016)), in situations where transitions were not too different, such as focus between asphalt to paved.…”

Section: Related Workmentioning

confidence: 99%

Road surface detection and differentiation considering surface damages

Rateke,

von Wangenheim

2020

Preprint

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A challenge still to be overcome in the field of visual perception for vehicle and robotic navigation on heavily damaged and unpaved roads is the task of reliable path and obstacle detection. The vast majority of the researches have as scenario roads in good condition, from developed countries. These works cope with few situations of variation on the road surface and even fewer situations presenting surface damages. In this paper we present an approach for road detection considering variation in surface types, identifying paved and unpaved surfaces and also detecting damage and other information on other road surface that may be relevant to driving safety. We also present a new Ground Truth with image segmentation, used in our approach and that allowed us to evaluate our results. Our results show that it is possible to use passive vision for these purposes, even using images captured with low cost cameras.

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“…There is a limitation in adaptively changing the threshold value change to the road environment, and the morphology operation also has a problem of providing robust performance only in a limited environment. M. Valente, et al, [4] proposed a road segmentation method based on vanishing point detection and seeded region growing algorithm. Their research is to set the boundary of the road area to vanishing point detection and then use the SRG algorithm to improve the accuracy of the road boundary.…”

Section: Introductionmentioning

confidence: 99%

Road Segmentation using Semantic egmentation Networks for ADAS

Kim¹

2019

IJITEE

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In this paper, we propose a method to automatically segment the road area from the input road images to support safe driving of autonomous vehicles. In the proposed method, the semantic segmentation network (SSN) is trained by using the deep learning method and the road area is segmented by utilizing the SSN. The SSN uses the weights initialized from the VGC-16 network to create the SegNet network. In order to fast the learning time and to obtain results, the class is simplified and learned so that it can be divided into two classes as the road area and the non-road area in the trained SegNet CNN network. In order to improve the accuracy of the road segmentation result, the boundary line of the road region with the straight-line component is detected through the Hough transform and the result is shown by dividing the accurate road region by combining with the segmentation result of the SSN. The proposed method can be applied to safe driving support by autonomously driving the autonomous vehicle by automatically classifying the road area during operation and applying it to the road area departure warning system

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Real-time method for general road segmentation

Cited by 10 publications

References 11 publications

Road Surface Classification with Images Captured From Low-cost Camera - Road Traversing Knowledge (RTK) Dataset

Road Surface Classification with Images Captured From Low-cost Camera - Road Traversing Knowledge (RTK) Dataset

Road surface detection and differentiation considering surface damages

Road Segmentation using Semantic egmentation Networks for ADAS

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