# Performance Evaluation of Several Transfer Learning Models for Classification of Road Surface State

Fahim Ur Rahman, Md. Tanvir Ahmed, Emran Khan, Md Mahfuzur Rahman, Shafin Ahamed, Shahriar Mamun, Md Mehedi Hasan

<figure><img src="image_1.png" /></figure>

## To cite this version:

Fahim Ur Rahman, Md. Tanvir Ahmed, Emran Khan, Md Mahfuzur Rahman, Shafin Ahamed, et al.: Performance Evaluation of Several Transfer Learning Models for Classification of Road Surface State. 6th International Conference on Computational Intelligence in Data Science (ICCIDS), Feb 2023, Chennai, India. pp.76-88, 10.1007/978-3-031-38296-3_7. hal-05466035

HAL Id: hal-05466035

https://inria.hal.science/hal-05466035v1

Submitted on 19 Jan 2026

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
<figure><img src="image_3.png" /></figure>

ifip

International Federation for Information Processing

This document is the original author manuscript of a paper submitted to an IFIP
conference proceedings or other IFIP publication by Springer Nature. As such, there
may be some differences in the official published version of the paper. Such
differences, if any, are usually due to reformatting during preparation for publication or
minor corrections made by the author(s) during final proofreading of the publication
manuscript.
Performance Evaluation of Several Transfer
Learning Models for Classification of Road
Surface State

Fahim Ur Rahman¹(✉), Md. Tanvir Ahmed¹, Emran Khan², Md Mahfuzur Rahman¹,
Shafin Ahamed¹, Shahriar Mamun¹, and Md Mehedi Hasan¹

¹ Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh
{fahim15-13674,tanvir15-13602,mahfuzur15-12378,shafin15-12201, shahriar15-12859,mehedi15-14030}@diu.edu.bd

² Department of English, Daffodil International University, Dhaka, Bangladesh
emrankhan.eng@diu.edu.bd

**Abstract.** Using a customized approach, automatic classification of road surface condition and categorized data storing are proposed using DenseNet201. Road surface distress is one of the main issues affecting transportation safety. The first indication of a catastrophic asphalt pavement collapsing is a surface crack, that can later develop into a pothole and result in high repair costs. By replacing the surveillance system with the automated software program that we are recommending in this analysis, the traditional methods for identifying cracks or degradation in a road's surface, which involved manual examination by people, can be eliminated. DenseNet201 has outperformed other compared models with an accuracy of 98.75% and the most minimal model loss while testing while classifying damaged and smooth road surfaces. Later, certain governing bodies responsible for preserving the quality of road infrastructure can use the model's classified images.

**Keywords:** Road Surface · Transfer Learning · Computer Vision · DenseNet201

# 1 Introduction

In recent years, deep learning-based transfer learning algorithms have had a significant impact on increasing the classification accuracy of various types of image datasets. Researchers had built so many transfer learning models, all of which performed pretty decently on any balanced image dataset. With the growing number of deep learning-based transfer learning models, the behavior of these models is an ever-changing phenomenon given different kinds of datasets. There have been many observations in previous research studies on the evaluation of many transfer learning models. With the recent unimaginable development of deep learning algorithms, the number of research studies on different kinds of topics with very complex scenarios of datasets has increased overnight. However, in those studies, transfer learning-based algorithms were used, but evaluation of
the performance of those transfer learning algorithms has not been a topic of interest in those studies. The performance of transfer learning models can vary for several reasons. Dataset complexity, Dataset imbalance, Data quality of the dataset, Background layers of the transfer learning models, etc. are some of the notable reasons for performance differences [1].

Dataset imbalance can cause a transfer learning model to perform worse than an average transfer learning model's performance trained on a balanced dataset. A portion of a bigger dataset, in this case a smaller target dataset, can freeze the layers in the initial phase of the transfer learning-based DNN network, which is trained on this particular dataset and gives a fairly high performance on the accuracy of the target classes [2]. The training accuracy, validation accuracy, and test accuracy surely vary from each other for any kind of deep learning-based algorithm. But for the transfer learning models, this changing behavior is like following the chaos rule, as almost every transfer learning model performs very differently on training accuracy, validation accuracy, and test accuracy. This can be for many reasons. The most significant reason, however, is the complex structure and quality of the dataset used for training and testing the transfer learning models [3].

From this observation, this research study follows a complex and custom dataset named "Road Surface State," implementing several mostly used and high-performing transfer learning models, and evaluating the performance of these transfer learning models on every possible aspect in contrast to the dataset used for implementing the models. As the structure and quality of the training and testing datasets impact a lot on the performance of the transfer learning models, the processes of data preprocessing and data augmentation are utilized on the dataset to quantify the quality and balance the size of the dataset. This gave an opportunity to differentiate the performance of the implemented transfer learning models on two dimensions as the datasets have been on two versions. The following list demonstrates the contributions this research study has to offer, along with a detailed explanation of their overall value for research that makes further use of transfer learning models.

1. Several of the best-performing and most-used transfer learning models have been implemented in this study on a broader scale with fine-tuning as well to reach the maximum potential that the transfer learning model has to offer.

2. The transfer models have been built through training, validation, and testing with a complex dataset to stretch the models to their limits, which helped in the long run for evaluating the performance of those models.

3. An augmented dataset built from the initially used dataset was also used in the implementation of the transfer learning models to further intensify the possible ground for evaluating the performances from a different angle. This process had a significant impact on the clear visibility of the evaluation of the transfer learning model's performance.

4. The comparison of performance in different aspects like training accuracy, test accuracy, training loss, validation loss, etc. between the implemented transfer learning
models has been shown with a proper visualization that gives a clear overview of the performance gaps of the implemented models.

The remainder of this research study is divided into the following sections. In the next section, which is Sect. 2, an overview of the background study of this research is presented. Section 3 describes the methodology of the entire study, including a descriptive analysis of all the processes involved. Section 4 is the results and analysis part of this study. The conclusion of the study with mention of future work is given in Sect. 5.

## 2 Literature Review

To determine various kinds of road surfaces and to differentiate road surface cracks autonomously, Bhagyalaxmi Behera and Rishi Sikka implemented a method consisting of different deep learning techniques like CNN, LSTM, and RC. The testing phase revealed that the utilized model can score a decent score on precision in solving the problem [4]. To eliminate the limitation of road surface cracks becoming fractured or invisible in the process of binarization in the process of digital images analysis Lin Meng et al. utilized a method using the HOG algorithm. As the result of the test phase is satisfactory, the method remains to be effective in real life scenarios [5].

N. T. Sy et al. proposed a method for crack detection using the AMAC system. The method was developed using a real life set of images consisting of 2834 images of two classes: No defect and Crack images. The overall accuracy of the proposed system is about 93% which is significantly higher than other implementations like Laboratory and Static condition system [6]. Feng Zhang et al. suggested a method to dismantle the problems like poor regional selectivity, high time complexity, and poor feature robustness of traditional target detection algorithms used to detect road marking. The suggested method utilized several deep learning techniques with a self-builtroad marking dataset, which consists of 36,811 images. The best-scoring deep learning algorithm in this case was R-FCNN with an 18-layer residual network, which scored a mAP of 90.7% [7]. To eliminate the imbalance problem of a dataset for implementing DNN and getting the best result from it, Wansik Choi et al. proposed a method of data augmentation named CycleGAN. The authors implemented several deep learning techniques with a raw dataset and with an augmented dataset augmented by CycleGAN augmentation techniques and endorsed a huge performance boost to 86% from 80% from the raw to the augmented dataset. The raw dataset has a total of 806 images and the augmented one got the number to 3306 images [8]. Willy Anugrah Cahyadi et al. proposed an effective road surface detection method based on visible light communication, also known as VLC. The method uses light emitting diodes based on vehicle headlights to illustrate the road surface and to gather the diffuse and specular reflections from the surface itself. The authors administered all the experiments including detection scheme and accuracy validation in an indoor environment. The accuracy validation suggests a VLC based road surface detection system can measure the wet level on the surface very precisely [9].

Roberto Medina et al. developed an extensive autonomous visual inspection method for identifying fractures in the surface of roads using LRIS technology. The method was implemented by training and testing with a dataset having a total of 3422 images of two classes: transverse cracks and longitudinal cracks. The Adaboost algorithm used in the
proposed method resulted in a balanced accuracy of 96.6% for transverse cracks along with 93.3% for longitudinal cracks [10]. An autonomous detection system of road surface cracks has been developed by utilizing mobile profile laser scan data. The implementation of the method includes a creation of training data using the K-means clustering algorithm for training the model using the RF algorithm. The implemented method scored an overall accuracy of 79% [11]. Meiqing Wu et al. suggested an efficient nonparametric depth-based road surface-identifying algorithm influenced by four inherent road features underneath stereo geometry, which is slightly different from the existing approach that is based on rigid mathematical models. The proposed method can cope with 7 and handle high-dynamic road scenarios. The authors implemented the method using benchmark datasets (KITTI, Daimler, etc.), and the method performed with a substantially high accuracy of over 90% on all occasions [12].

A robust road surface crack detection algorithm for maintaining both accuracy and computational efficiency has been presented by Rui Fan et al. Three different versions of KITTI benchmark datasets have been utilized for the training and validation of the proposed algorithm. The proposed algorithm scored an overall accuracy of 99.64%, which is a pretty satisfactory performance [13]. Lushan Cheng and his team of researchers proposed a deep-learning approach for classifying different types of road surface conditions and formed a new activation function based on the ReLu activation function. There were 400 images for testing and 1600 images for training in the preprocessed dataset [14]. Seungbo Shim et al. utilized a novel DNN architecture from scratch for detecting road surface conditions, keeping a motive for building a sustainable road environment. The algorithm was trained and tested with a custom dataset of 1700 images. The proposed algorithm scored an overall accuracy of 97.61%, which compared to other DNN-based architectures implemented by the authors outperformed all of them [15].

A CNN based road surface crack detection system has been suggested by the authors with semi-supervised learning. The dataset utilized in training and testing was augmented and results in a total of 40,536 images. The semi-supervised based algorithm scored an overall accuracy of 0.9387 which is higher than the supervised learning-based algorithm [16]. Thiago Rateke and Aldo von Wangenheim presented an approach for road surface detection that considered different kinds of surfaces, such as paved and unpaved surfaces, and also the detection of cracks. The authors used CNN-based algorithms by training and testing the RTK dataset consisting of 77,547 images in U-NET with the combination of ResNet34 architecture in a transfer learning approach. The developed algorithm achieved an overall accuracy of 97%, which suggests the premium capability of the suggested approach [17]. M. Yadav et al. conducted a case study on MLS data points along a wide street in a rural area of south-west India. These MLS data points were used for the training and testing of the proposed algorithm, which was later evaluated by some of the most common validation equations. This method was successfully built and attained an accuracy of 94.23% [18].

A detection system of road surface that uses a tripod equipped with Raspberry Pi was utilized by Junyi Wang et al. The road surface detection system was developed with GBDT, RNN and LSTM algorithms. The training and testing phase was completed with a total of 8000 rows of data which made a huge influence on the accuracy of the algorithms. The highest performing algorithm among the implemented algorithms
was gradient boosting decision tree, with a preciseness of 97.92% [19]. Saurabh Tiwari et al. presented an approach based on deep learning for a system that can monitor road surface conditions using GPS sensors and accelerometers. The authors developed and proposed an unsupervised learning algorithm named K-means clustering that can cluster and categorize the images fed to the algorithm. The final custom dataset consists of a total of 43786 labeled observations. The proposed algorithm scored a high accuracy of 98.5% in terms of three-way classification [20]. Braian Varona et al. proposed a deep learning-based utilization that can categorize various kinds of road surfaces with an automated distinguishability of potholes from road surface instability brought on by speed bumps or careless driving. For this proposed method, the author developed several models, among which CNN scored the highest accuracy of 98% on a road surface state dataset consisting of two classes: plot holes and no plot holes [21].

Kai Cordes et al. proposed a bigger scaled dataset for the measurement of road surface condition and wetness estimation by enabling camera-based road surface images. The dataset consists of 720,000 images recorded from a test track synchronized with high resulted video which is mostly accurate on water film height managements [22]. Sadia Mubashshira et al. suggested a novel approach to detecting road surfaces via color histogram analysis of road surfaces. The authors utilized a dataset consisting of 200 2D road surface images collected both from the real world and also from the internet for the proposed model to develop a segmentation approach with K-means clustering and Otsu thresholding for the purpose of detecting road surface cracks. The utilized model scored a notable accuracy of 97.75% [23].

# 3 Methodology

<figure><img src="image_4.png" /><figcaption>Fig. 1. Workflow of Methodology</figcaption></figure>

The methodology section contains detailed information about the dataset preparation, dataset preprocessing, augmenting the dataset and the models we have used for the classification. The Fig. 1 shows a generic workflow of the methodology.

## 3.1 Dataset Properties

We have utilized a custom-made dataset from various locations in Dhaka city of Bangladesh, we have used previously collected images of various roads for identifying road surfaces [24]. We tried to collect the images without having any unnecessary
obstacles to train the models more accurately. A sample of the collected dataset is visualized in Fig. 2. The dataset contains 640 raw images. For training and testing 256 (80%) and 32 (10%) images were taken from the raw dataset. For validation 32 (10%) images were chosen.

<figure><img src="image_5.png" /><figcaption>Fig. 2. Dataset Sample</figcaption></figure>

## 3.2 Dataset Cleaning

As our dataset is of road surfaces, we had to be extra careful of distractions. After collecting the dataset, we checked all of the images for images that were blurry or images with too much exposure. After checking, we stored the dataset in the cloud storage for any kind of security issues. Dataset Preprocessing: As we collected the dataset with a camera capable of capturing high-resolution images, the images were too large and unsuitable for testing models with them. For this issue, we had to rescale the images using (1./255), which alters the range of the pixels from (0,255) to (0,1). Before preprocessing, the dataset was 1080\*2400 pixels, which changed to 448\*448 pixels after preprocessing.

## 3.3 Dataset Augmentation

After preprocessing, a few images were left in the dataset. To increase the size of the dataset, we needed to make some modifications to the raw images. At first, we rotated the images between -15 degrees to + 15 degrees and sheared the images in a range of 0.2. Flipped the images horizontally and vertically. We fixed the ImageDataGenerator method to constant so that the degraded portions of the image only contain black. After augmentation, 3200 images were created from the raw images. From augmented images, 2560 (80%) images were chosen for training the models and 320 (10%) images for testing. 320 (10%) images were chosen for validation (Fig. 3).
<figure><img src="image_6.png" /><figcaption>Fig. 3. Augmented Dataset Sample</figcaption></figure>

## 3.4 Implemented Transfer Learning Models

We developed Eight different neural network-based models for our proposed system architecture. From those eight models, the model that generated the highest accuracy is our proposed system architecture. The DNN-based models implemented are Xception, InceptionV3, MobileNetV2, 4. Inception-ResNet-v2, VGG16, VGG19 and Densnet201, with said DensNet201 outperforming all of the implemented models in terms of accuracy of prediction. An overview of all the developed models and an explanation of the proposed model DensNet201 is provided below.

### Xception

Xception is made up of 71 layers, including Convolution layers. ImageNet, which is a benchmark database that has over a million images, is pre-trained by the Xception. Xception can classify images into over a thousand different object categories, including living and non-living objects. The model accepts images with a resolution of 299\*299 pixels by default.

### InceptionV3

InceptionV3, A convolutional neural network that is from the family of Inception. InceptionV3 uses Label Smoothing and 7\*7 factorized convolutions. Utilizing an auxiliary classifier generates label information further down the network.

### MobileNet-v2

MobileNetV2 is a CNN-based network that is grounded on an inverted residual structure. Between bottleneck layers, the residual connections take place. From MobileNetV1, the MobileNetV2 model was developed. MobileNetV1 and MobileNetV2, both take
pictures as input with the size of 224*224*3 pixels. At the preliminary convolution layer, MobileNetV2 contains 32 filters after those 19 inverted residual bottleneck layers are inserted.

## Inception-ResNet-v2

Using over a million images from the database of ImageNet of Inception the convolutional neural network Inception-ResNet-v2 was trained. ResNetV2 can classify image data into 1000 different object categories. The ResNetV2 network is of 164 layers. That is why ResNetV2 has established a quite rich feature characterization for a diverse set of images.

## VGG16

The model VGG16 is one of the best vision model architectures so far. It takes image data as input with a size of dimensions 224*224*3. It has a unique feature in that. Instead of working with a large number of hyper-parameters, it focuses on the convolution layers of the 3*3 filter.

## VGG19

The model VGG19 has nineteen layers. Where sixteen layers are convolution layers, five MaxPool layers, three fully joined layers, and a SoftMax layer. With an image input size of 224*224, the model works as default. It uses the kernel of 3*3 size with a stride size of 1, which facilitates the model to cover the whole image.

## 3.5 Proposed DNN-Based Transfer Learning Models

### DenseNet201

From recent work, it has been seen that convolutional networks could be significantly deeper, more efficient and more accurate. If they have shorter connections amongst the layers at the input and those near the output, they should be easier to train. DensNet201 is a convolutional neural network-based neural network. It consists of 201 layers. It is a pre-trained network, with over a million images from the ImageNet database. The network is capable of classifying images into thousands of different object categories, such as a mouse, pen, animal, etc. It has an image input size of 224*224 (Fig. 4).

## 4 Results Evaluation

Implementation of the above-described transfer learning models were performed using Google Colaboratory and the programming language used is Python. The experimental procedure included Intel® Xeon® CPU at 2.00 GHz with memory of 13 GB while GPU acceleration enabled. As Google Colab allows specific time for a particular session to run, some adaptive measures were taken in the parameter settings. Finally, each of the model's evaluation metrics are compared in a tabular format to find out the best performing model in the same exact training setting for each of them.
<figure><img src="image_7.png" /><figcaption>Fig. 4. Model Architecture of DenseNet201</figcaption></figure>

## 4.1 Performance of Implemented Models

Using raw and augmented versions of the custom collected dataset, 7 different DNN models have been tested with the approach of transfer learning. Among several instances of experimentation, varieties of combination of model loss and accuracy have been seen. The transfer learning strategy in this case enables us to train using our particular dataset while preserving the pre-trained weights of each model. The overall performances of models regarding the testing loss and testing accuracy is shown in Table 1.

<table><caption>**Table 1.** Performance of the Implemented Models</caption><thead><tr><th>Transfer Learning Model</th><th>Dataset State</th><th>Model Loss</th><th>Model Accuracy</th></tr></thead><tbody><tr><td rowspan="2">VGG16</td><td>Raw</td><td>13.9874</td><td>88.39%</td></tr><tr><td>Augmented</td><td>0.56683</td><td>93.57%</td></tr><tr><td rowspan="2">VGG19</td><td>Raw</td><td>8.2523</td><td>83.75%</td></tr><tr><td>Augmented</td><td>0.52181</td><td>93.12%</td></tr><tr><td rowspan="2">Xception</td><td>Raw</td><td>12.5104</td><td>83.50%</td></tr><tr><td>Augmented</td><td>0.5334</td><td>89.06%</td></tr><tr><td rowspan="2">DenseNet169</td><td>Raw</td><td>10.6197</td><td>87.50%</td></tr><tr><td>Augmented</td><td>0.4398</td><td>89.73%</td></tr><tr><td rowspan="2">InceptionV3</td><td>Raw</td><td>14.8056</td><td>91.75%</td></tr><tr><td>Augmented</td><td>0.6053</td><td>95.62%</td></tr><tr><td rowspan="2">InceptionResNetV2</td><td>Raw</td><td>14.0289</td><td>92.78%</td></tr><tr><td>Augmented</td><td>0.3273</td><td>95.61%</td></tr><tr><td rowspan="2">DenseNet201</td><td>Raw</td><td>3.1830</td><td>93.75%</td></tr><tr><td>Augmented</td><td>0.3271</td><td>98.75%</td></tr></tbody></table>
## 4.2 Generating Confusion Matrix

Each model's confusion matrix, which is based on the model's forecasts, is created after a successful training session and provides a summary of the model's overall performance. The confusion matrix's true-positives, true-negatives, false-positives, and false-negatives values are typically specified as four separate numbers. Comparing the best performing model from Table 1, the heatmap of confusion matrix of the model DenseNet201 shows 160 correct prediction of damaged road surface and 156 accurate prediction of smooth road surfaces. The generated confusion matrix is shown in Fig. 5.

<figure><img src="image_8.png" /><figcaption>Fig. 5. Confusion Matrix of DenseNet201</figcaption></figure>

## 4.3 Generating Classification ROC

Initially, all the models were run for 50 epochs that resulted in generating loss and validation curves for training that can be visualized using the history plotting of the models. For ease of comparison, training and validation loss followed by training and validation accuracy have been visualized in the Fig. 6.

The depicted curves demonstrate that the transfer learning model's best accuracy was attained at epoch 12 and that the best training loss was recorded at epoch 50. The suggested method that followed the aforementioned curves didn't have an overfitting problem. When a model overfits the training set of data and is poor at generalization, it will perform poorly when used to analyze fresh data. A validation phase can be performed after each training iteration of the deep learning model to look for overfitting. The validation process involves testing the network with the most recent weight updates using validation data that isn't present in the training data. Figure 6 shows a graph of the accuracy and loss values for each repetition of the training phase that entailed establishing and validating data. An overfit model often experiences a decrease in the
<figure><img src="image_9.png" /><figcaption>Fig. 6. Training-Validation Loss and Accuracy Curves</figcaption></figure>

value of the training loss but an increase in the value of the validation loss as the training process iterates. The training loss and validation loss both continue to decrease with each training cycle, as seen in Fig. 6(a). As a result, it can be concluded that the proposed model is both acceptable for generalization and not overfit. Due to the suggested model’s use of data augmentation and a small epoch value, which increases the variety of the training samples, this is made achievable.

## 5 Conclusion

The implemented DNN-based transfer learning models performed with higher accuracy in tests. Among them, DenseNet201 is the best-performing transfer learning model. The result and analysis portion of the study clearly show that the algorithm, which had less loss in the training phase, had an overall better performance in the test accuracy. Another notable evaluating fact is that, after balancing the dataset, the transfer learning models trained on the augmented dataset performed way better on classifying road surface state than those previously trained on the non-augmented dataset.

The background layers of the transfer learning models can be a supporting factor for the variety in accuracy of different transfer learning models. The future work of this study will focus on evaluating the performance drop and loss after altering the background layers of the transfer learning models.

## References

1. Gordon, M.A., Duh, K., Andrews, N.: Compressing bert: Studying the effects of weight pruning on transfer learning. arXiv preprint arXiv:2002.08307 (2020)

2. Soekhoe, D, van der Putten, P., Plaat, A.: On the impact of data set size in transfer learning using deep neural networks. In: International symposium on intelligent data analysis, pp. 50–60. Springer, Cham (2016)

3. Zhuang, F., et al.: A comprehensive survey on transfer learning. Proc. IEEE 109(1), 43–76 (2020)
4. Behera, B., Sikka, R.: Deep learning for observation of road surfaces and identification of path holes. Materials Today: Proceedings (2021)

5. Meng, L., Wang, Z., Fujikawa, Y., Oyanagi, S.: Detecting cracks on a concrete surface using histogram of oriented gradients. In: 2015 International Conference on Advanced Mechatronic Systems (ICAMechS), pp. 103–107. IEEE (2015)

6. Sy, N.T., Avila, M., Begot, S., Bardet, J.-C.: Detection of defects in road surface by a vision system. In: MELECON 2008-The 14th IEEE Mediterranean Electrotechnical Conference, pp. 847–851. IEEE (2008)

7. Zhang, F., Wu, X., Gu, C.: Detection of road surface identifiers based on deep learning. In: 2019 International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM), pp. 66–70. IEEE (2019)

8. Choi, W., Heo, J., Ahn, C.: Development of road surface detection algorithm using CycleGAN-augmented dataset. Sensors **21**(22), 7769 (2021)

9. Cahyadi, W.A., Kim, Y.-H., Chung, Y.-H., Ghassemlooy, Z.: Efficient road surface detection using visible light communication. In: 2015 Seventh International Conference on Ubiquitous and Future Networks, pp. 61–63. IEEE (2015)

10. Medina, R., Llamas, J., Zalama, E., Gómez-García-Bermejo, J.: Enhanced automatic detection of road surface cracks by combining 2D/3D image processing techniques. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 778–782. IEEE (2014)

11. Van Der Horst, B.B., Lindenbergh, R.C., Puister, S.W.J.: Mobile laser scan data for road surface damage detection. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences **42**(2/W13) (2019)

12. Wu, M., Lam, S.-K., Srikanthan, T.: Nonparametric technique based high-speed road surface detection. IEEE Trans. Intell. Transp. Syst. **16**(2), 874–884 (2014)

13. Fan, R., Ozgunalp, U., Hosking, B., Liu, M., Pitas, I.: Pothole detection based on disparity transformation and road surface modeling. IEEE Trans. Image Process. **29**, 897–908 (2019)

14. Cheng, L., Zhang, X., Shen, J.: Road surface condition classification using deep learning. J. Vis. Commun. Image Represent. **64**, 102638 (2019)

15. Shim, S., Kim, J., Lee, S.-W., Cho, G.-C.: Road surface damage detection based on hierarchical architecture using lightweight auto-encoder network. Autom. Constr. **130**, 103833 (2021)

16. Chun, C., Ryu, S.-K.: Road surface damage detection using fully convolutional neural networks and semi-supervised learning. Sensors **19**(24), 5501 (2019)

17. Rateke, T., von Wangenheim, A.: Road surface detection and differentiation considering surface damages. Auton. Robot. **45**(2), 299–312 (2021). https://doi.org/10.1007/s10514-020-09964-3

18. Yadav, M., Lohani, B., Singh, A.K.: Road surface detection from mobile lidar data. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences **4**(5) (2018)

19. Wang, J., Meng, Q., Shang, P., Saada, M.: Road surface real-time detection based on Raspberry Pi and recurrent neural networks. Trans. Inst. Meas. Control. **43**(11), 2540–2550 (2021)

20. Tiwari, S., Bhandari, R., Raman, B.: Roadcare: a deep-learning based approach to quantifying road surface quality. In: Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable Societies, pp. 231–242 (2020)

21. Varona, B., Monteserin, A., Teyseyre, A.: A deep learning approach to automatic road surface monitoring and pothole detection. Pers. Ubiquit. Comput. **24**(4), 519–534 (2019). https://doi.org/10.1007/s00779-019-01234-z

22. Cordes, K., Reinders, C., Hindricks, P., Lammers, J., Rosenhahn, B., Broszio, H.: RoadSaW: A Large-Scale Dataset for Camera-Based Road Surface and Wetness Estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4440–4449 (2022)
23. Mubashshira, S., Azam, M.M., Ahsan, S.M.M.: An unsupervised approach for road surface crack detection. In: 2020 IEEE Region 10 Symposium (TENSYMP), pp. 1596–1599. IEEE (2020)

24. Rahman, F.U., Ahmed, M.T., Amin, M.R., Nabi, N., Ahamed, M.M.S.: A Comparative Study on Road Surface State Assessment Using Transfer Learning Approach. In: 2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT), pp. 1–6. IEEE (2022)