Computer Vision Applied to Road Lines Recognition Using Machine Learning

Rodríguez-Garavito, C. H.; Ponz, Aurelio; García, Fernando; Escalera, Arturo de la; Armingol, José María

doi:10.15849/icit.2015.0017

Cited by 5 publications

(5 citation statements)

References 8 publications

(7 reference statements)

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“…In [5], the sensor data were discretised into distinct cells and random sample consensus (RANSAC) was applied for fitting to the lowest Z coordinate inside the cells. Rodríguez-Garavito et al also used a RANSAC algorithm but applied it to a nearly complete sensor point cloud [6]. In [7], RANSAC algorithm was used to fit planes into a single plane.…”

Section: Point Cloud Segmentation On Road Surfacementioning

confidence: 99%

Road classification using 3D LiDAR sensor on vehicle

Liu

Zhao

Zhang

2023

Meas. Sci. Technol.

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Road classification is essential in outdoor vehicle perception systems to evaluate driving scenarios. A light detection and ranging (LiDAR) sensor can provide accurate measurements of an environment composed of range, intensity, and angular information. This study proposes a weakly supervised learning algorithm for classifying road surface types based on laser measurements. The proposed algorithm first separates the laser points on the road surface based on the angular structure of the initial range measures obtained using 3D LiDAR in a noniterative manner. Then, a physical-data-driven model that represents the retroreflective nature of the different surface types is abstracted from the intensity measures and their factors. The linear regression parameters of the model are employed as feature descriptions of the road surface. The K-means method is used to classify the different road types. The learning process requires weak supervision at the training centroid stage and is robust to new road types. The classification performance was evaluated in different outdoor scenarios, and the classification accuracies exceeded 98%. The proposed method outperforms similar existing methods, performs at the same frequency as that of LiDAR, and can be implemented in real time.

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Section: Point Cloud Segmentation On Road Surfacementioning

confidence: 99%

Road classification using 3D LiDAR sensor on vehicle

Liu

Zhao

Zhang

2023

Meas. Sci. Technol.

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“…Lee et al [12] proposed a method to detect lane color using support vector machine. However, the classifier based methods for lane type [5], [6], [7] are highly data dependent and computationally intensive and histogram methods [4] face issues in curvy road scenarios. Color segmentation methods for lane color [9], [10] suffer during illumination variations and image sensor settings which are very common in autonomous driving environment.…”

Section: A D V a N C E S I N I M A G E A N D V I D E O P R O C E S S mentioning

confidence: 99%

Lane Characterization Under Challenging Scenarios for Autonomous Driving System

Suddamalla

Singal

Mohan

2016

AIVP

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Advanced Driver Assistance systems (ADAS) have seen increasing popularity due to their importance in automotive driver safety and autonomous driving. These systems analyze data from several sensors mounted on the vehicle to detect lanes, obstacles and traffic conditions to ensure safe driving. Lane markings on road with different color and structure provide information on safe drive zone and other traffic restrictions on road. Typical ADAS solutions depend on vision based sensors for lane detection.Here we propose an efficient algorithm for detecting type and color of the lane marks as this information plays critical role in taking the decision for safety features such as lane change and lane keep assist. Our algorithm is pluggable to any state-of-art lane detection algorithm and provides lane type and color for straight, curvy roads. The proposed method is tested on various challenging scenarios and results are promising.

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“…Historically, the detection of such road maladies predominantly depended upon human inspection, a method that, despite its earnest intent, is laborious, time-intensive, and susceptible to oversights. The evolution of camera and laser technologies [1] has ushered in a new era, allowing for the capture of road images in high resolution, even while in transit. This has resulted in the accumulation of expansive datasets of road imagery.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Efficacy of Sparse Feature in Pavement Distress Image Classification: A Focus on Pavement-Specific Knowledge

Yuan,

Chen,

Lang

et al. 2023

Applied Sciences

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Road surface deterioration, such as cracks and potholes, poses a significant threat to both road safety and infrastructure longevity. Swift and accurate detection of these issues is crucial for timely maintenance and user security. However, current techniques often overlook the unique characteristics of pavement images, where the small distressed areas are vastly outnumbered by the background. In response, we propose an innovative road distress classification model that capitalizes on sparse perception. Our method introduces a sparse feature extraction module using dilated convolution, tailored to capture and combine sparse features of different scales from the image. To further enhance our model, we design a specialized loss function rooted in domain-specific knowledge about pavement distress. This loss function enforces sparsity during feature extraction, guiding the model to align precisely with the sparse distribution of target features. We validate the strength and effectiveness of our model through comprehensive evaluations of a diverse dataset of road images containing various distress types and conditions. Our approach exhibits significant potential in advancing traffic safety by enabling more efficient and accurate detection and classification of road distress.

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Computer Vision Applied to Road Lines Recognition Using Machine Learning

Cited by 5 publications

References 8 publications

Road classification using 3D LiDAR sensor on vehicle

Road classification using 3D LiDAR sensor on vehicle

Lane Characterization Under Challenging Scenarios for Autonomous Driving System

Exploring the Efficacy of Sparse Feature in Pavement Distress Image Classification: A Focus on Pavement-Specific Knowledge

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