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 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 surfaces that may be relevant to driving safety. Our approach makes use of Convolutional Neural Networks (CNN) to perform semantic segmentation, we use the U-NET architecture with ResNet34, in addition we use the technique known as Transfer Learning, where we first train a CNN model without using weights in the classes as a basis for a second CNN model where we use weights for each class. 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.

Road surface detection and differentiation considering surface damages

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

Monocular vision based road detection methods are mostly based on machine learning methods, relying on classiﬁcation and feature extraction accuracy, and suffer from appearance, illumination and weather changes. Traditional methods introduce the predictions into conditional random ﬁelds or markov random ﬁelds models to improve the intermediate predictions based on structure. These methods are optimization based and therefore resource heavy and slow, making it unsuitable for real time applications. We propose a method to detect and segment roads with a random forest classiﬁer of local experts with superpixel based machine-learned features. The random forest takes in machine learnt descriptors from a pre-trained convolutional neural network - VGG-16. The features are also pooled into their respective superpixels, allowing for local structure to be continuous. We compare our algorithm against Nueral Network based methods and Traditional approaches (based on Hand-crafted features), on both Structured Road (CamVid and Kitti) and Unstructured Road Datasets. Finally, we introduce a Road Scene Dataset with 1000 annotated images, and verify that our algorithm works well in non-urban and rural road scenarios. 1

Unstructured Road Segmentation using Hypercolumn based Random Forests of Local experts

We present a literature review to analyze the state of the art in the area of road detection based upon frontal images. For this purpose, a systematic literature review (SLR) was conducted that focuses on analyzing region-based works, since they can adapt to different surface types and do not depend on road geometry or lane markings. Through the comprehensive study of publications in a 11-year time frame, we analyze the methods that are being used, on which types of surface they are applied, whether they are adaptive in relation to surface changes, and whether they are able to distinguish possible faults or changes in the road, such as potholes, shadows, and puddles.

Passive Vision Region-Based Road Detection: A Literature Review

Road detection is a key task for autonomous land vehicles. Monocular vision-based road-detection algorithms are mostly based on machine learning approaches and are usually cast as classification problems. However, the pixel-wise classifiers are faced with the ambiguity caused by changes in road appearance, illumination and weather. An effective way to reduce the ambiguity is to model the contextual information with structured learning and prediction. Currently, the widely used structured prediction model in road detection is the Markov random field or conditional random field. However, the random field-based methods require additional complex optimization after pixel-wise classification, making them unsuitable for real-time applications. In this paper, we present a structured random forest-based road-detection algorithm which is capable of modelling the contextual information efficiently. By mapping the structured label space to a discrete label space, the test function of each split node can be trained i...

https://journals.sagepub.com/doi/pdf/10.5772/63561

Monocular Road Detection Using Structured Random Forest

In this paper, a fast particle filer based unstructured road detection and tracking algorithm is presented. We take the parameters of the road model and the relative pose of the vehicle with respect to the road as the state vector. The pixels of the test image are classified by learned boosted classifier based on rich pixel features to get a probabilistic output. For each particle, the virtual road image in the perspective view is generated according to the state vector. The particles are then weighted by the consistency of the virtual road image with the probability map. Then we can can estimate the optimal state with the particle weights. We further propose a scheme to accelerate the algorithm substantially with little degeneracy in performance by measuring the consistency with only several rows instead of the whole image. Extensive experiments show that the proposed method can detect and track the road robustly in various unstructured environments within real time.

http://ieeexplore.ieee.org/iel7/7140653/7161655/07162618.pdf

Fast unstructured road detection and tracking from monocular video

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Fast unstructured road detection and tracking from monocular video