Classification of road surfaces using convolutional neural network

Balcerek, Julian; Konieczka, Adam; Piniarski, Karol; Pawlowski, Pawel

doi:10.23919/spa50552.2020.9241254

Cited by 7 publications

(4 citation statements)

References 16 publications

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“…In [96], the authors trained and tested AlexNet on 500 original images from Poland roads from which nine types of road surfaces were labeled: asphalt, concrete, trylinka (road surface made of large hexagonal concrete paver blocks), concrete paver blocks, granite paver blocks, openwork surface, gravel, sand, and grass. The study conducted two sets of experiments: one for recognizing specific types of road surfaces and another for determining the general condition of the surfaces.…”

Section: The Nature Of Surface Materialsmentioning

confidence: 99%

A Review of Deep Learning Advancements in Road Analysis for Autonomous Driving

Botezatu,

Burlacu,

Orhei

2024

Applied Sciences

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The rapid advancement of autonomous vehicle technology has brought into focus the critical need for enhanced road safety systems, particularly in the areas of road damage detection and surface classification. This paper explores these two essential components, highlighting their importance in autonomous driving. In the domain of road damage detection, this study explores a range of deep learning methods, particularly focusing on one-stage and two-stage detectors. These methodologies, including notable ones like YOLO and SSD for one-stage detection and Faster R-CNN for two-stage detection, are critically analyzed for their efficacy in identifying various road damages under diverse conditions. The review provides insights into their comparative advantages, balancing between real-time processing and accuracy in damage localization. For road surface classification, the paper investigates the classification techniques based on both environmental conditions and material road composition. It highlights the role of different convolutional neural network architectures and innovations at the neural level in enhancing classification accuracy under varying road and weather conditions. The main finding of this work is that it offers a comprehensive overview of the current state of the art, showcasing significant strides in utilizing deep learning for road analysis in autonomous vehicle systems. The study concludes by underscoring the importance of continued research in these areas to further refine and improve the safety and efficiency of autonomous driving.

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Section: The Nature Of Surface Materialsmentioning

confidence: 99%

A Review of Deep Learning Advancements in Road Analysis for Autonomous Driving

Botezatu,

Burlacu,

Orhei

2024

Applied Sciences

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“… Fink et al (2020) went one step further by using SqueezeNet to reduce the computational complexity without significantly affecting the accuracy. Svensson (2020) and Balcerek et al (2020) utilized DenseNet121 and AlexNet to classify road-surface conditions and estimate the corresponding road friction. Dewangan & Sahu (2021) proposed a CNN model called RCNet to classify road-surface conditions, achieving an accuracy of 99%.…”

Section: Related Workmentioning

confidence: 99%

Multi-supervised bidirectional fusion network for road-surface condition recognition

Zhang,

Li,

Wang

et al. 2023

PeerJ Computer Science

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Rapid developments in automatic driving technology have given rise to new experiences for passengers. Safety is a main priority in automatic driving. A strong familiarity with road-surface conditions during the day and night is essential to ensuring driving safety. Existing models used for recognizing road-surface conditions lack the required robustness and generalization abilities. Most studies only validated the performance of these models on daylight images. To address this problem, we propose a novel multi-supervised bidirectional fusion network (MBFN) model to detect weather-induced road-surface conditions on the path of automatic vehicles at both daytime and nighttime. We employed ConvNeXt to extract the basic features, which were further processed using a new bidirectional fusion module to create a fused feature. Then, the basic and fused features were concatenated to generate a refined feature with greater discriminative and generalization abilities. Finally, we designed a multi-supervised loss function to train the MBFN model based on the extracted features. Experiments were conducted using two public datasets. The results clearly demonstrated that the MBFN model could classify diverse road-surface conditions, such as dry, wet, and snowy conditions, with a satisfactory accuracy and outperform state-of-the-art baseline models. Notably, the proposed model has multiple variants that could also achieve competitive performances under different road conditions. The code for the MBFN model is shared at https://zenodo.org/badge/latestdoi/607014079.

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“…Then, these are elaborated to get information, like the type of defect and its size, for instance. Balcerek et al [10] propose a classifier of road surfaces based on CNN that determines the general condition of surfaces. The development of new systems is going in the direction of replacing traditional cameras with smartphone cameras.…”

Section: State Of the Art And Mission Of The Paper A Related Workmentioning

confidence: 99%

An Embedded System for Acoustic Data Processing and AI-Based Real-Time Classification for Road Surface Analysis

2022

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The current roadway monitoring is expensive and not systematic. This paper proposes a new system able to evaluate the pavement quality of road infrastructure. The embedded system records and processes the acoustic data of the wheel-road interaction and classifies in real-time roadways' health thanks to integrated AI solutions. The measurements to produce the dataset to train a convolutional neural network (CNN) were collected using a vehicle operating at different cruise speeds in the area of Pisa. The dataset is composed by acoustic data belonging to several typologies of roads: dirty or grass roads, high roughness surfaces and roads with cracks or potholes. The raw audio signals were split, labelled, and converted into images by calculating the Mel spectrogram. Finally, the authors designed a tiny CNN with a size equal to 18 kB able to classify between four different classes: good quality road, ruined road, silence and unknown. The CNN architecture achieves an accuracy of about 93% on the original model and 90% on the quantized one. Quantization permits to convert the final architecture into a suitable form to be deployed on a low-complex embedded system integrated in the tyre cavity. In addition, a custom board was designed to act as IoT node thanks to a Bluetooth Low Energy communication towards smartphones and/or car infotainment systems. These systems, featured with GPS, guarantee to obtain real-time maps service of road quality. At authors' knowledge, this is the first real-time and fully integrated solution at the state of the art for road pavement quality analysis and classification on acoustic data.INDEX TERMS road surface classification; convolutional neural network; audio processing; embedded system; image recognition.

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Classification of road surfaces using convolutional neural network

Cited by 7 publications

References 16 publications

A Review of Deep Learning Advancements in Road Analysis for Autonomous Driving

A Review of Deep Learning Advancements in Road Analysis for Autonomous Driving

Multi-supervised bidirectional fusion network for road-surface condition recognition

An Embedded System for Acoustic Data Processing and AI-Based Real-Time Classification for Road Surface Analysis

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