Deep Learning based Detection of potholes in Indian roads using YOLO

Dharneeshkar, J.; Dhakshana, S. M.; Aniruthan, S A; Karthika, R.; Parameswaran, Latha

doi:10.1109/icict48043.2020.9112424

Cited by 48 publications

(5 citation statements)

References 13 publications

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“…Many approaches rely solely on computer vision techniques for pothole detection [6], [7], [9] - [19]. The inherent problem with using only computer vision-based approaches is that they are highly weather-dependent, and physical factors like fog or rain greatly affect the mapping process.…”

Section: Research Gapmentioning

confidence: 99%

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A Pothole Can Be Seen with Two Eyes: An Ensemble Approach to Pothole Detection

Patawar,

Mehdi,

Kore

et al. 2024

Preprint

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The issue of potholes and bad road quality is faced by nearly every developing country. These conditions often lead to accidents and traffic and cause the general public inconvenience. Significant research work has been performed in the field of effective pothole and road quality detection but due to several underlying issues like relying on only computer vision or sensor-based modeling, or requiring specialized equipment like LiDAR, implementing these solutions in the real world has never been feasible, especially for developing countries where a reliable system is required which works under any condition and more importantly, can be implemented with minimal resources and changes to the existing infrastructure. The proposed system is an end-to-end robust architecture that utilizes mobile devices mounted on government vehicles for data collection and features an innovative ensemble of a YOLOS and a YOLOv8 model resulting in a 97.34% mAP@0.50 score for pothole detection, in conjunction with state-of-the-art sensor based pothole detection and road quality detection models resulting in impressive accuracies of 98.5% and 95.4% respectively. This two-forked approach to pothole and road quality detection combined with innovative connected applications like an analytics dashboard for the government and a navigation application for the consumer make this entire system highly relevant while being scalable, reliable, and easily deployable using the existing infrastructure in developing countries.

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Section: Research Gapmentioning

confidence: 99%

“…They also integrated this model into an Android application for the end user. D.J et al [6] created a dataset for Indian Roads and performed a comparative study of several models under the YOLO family for computer vision-based Pothole Detection. Their study comprised YOLO v2, YOLO v3, and YOLO v3-tiny.…”

Section: Introductionmentioning

confidence: 99%

A Pothole Can Be Seen with Two Eyes: An Ensemble Approach to Pothole Detection

Patawar,

Mehdi,

Kore

et al. 2024

Preprint

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“…Numerous crack detection models have been developed in the literature as a result of recent developments in deep learning (DL), particularly the evolution of convolutional neural networks (CNNs) 29 . DL-based models for crack detection follow steps analogous to ML-based models described above.…”

Section: Literature Reviewmentioning

confidence: 99%

Transfer learned deep feature based crack detection using support vector machine: a comparative study

Bhalaji Kharthik,

Onyema,

Mallik

et al. 2024

Sci Rep

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Technology offers a lot of potential that is being used to improve the integrity and efficiency of infrastructures. Crack is one of the major concerns that can affect the integrity or usability of any structure. Oftentimes, the use of manual inspection methods leads to delays which can worsen the situation. Automated crack detection has become very necessary for efficient management and inspection of critical infrastructures. Previous research in crack detection employed classification and localization-based models using Deep Convolutional Neural Networks (DCNNs). This study suggests and compares the effectiveness of transfer learned DCNNs for crack detection as a classification model and as a feature extractor to overcome this restriction. The main objective of this paper is to present various methods of crack detection on surfaces and compare their performance over 3 different datasets. Experiments conducted in this work are threefold: initially, the effectiveness of 12 transfer learned DCNN models for crack detection is analyzed on three publicly available datasets: SDNET, CCIC and BSD. With an accuracy of 53.40%, ResNet101 outperformed other models on the SDNET dataset. EfficientNetB0 was the most accurate (98.8%) model on the BSD dataset, and ResNet50 performed better with an accuracy of 99.8% on the CCIC dataset. Secondly, two image enhancement methods are employed to enhance the images and are transferred learned on the 12 DCNNs in pursuance of improving the performance of the SDNET dataset. The results from the experiments show that the enhanced images improved the accuracy of transfer-learned crack detection models significantly. Furthermore, deep features extracted from the last fully connected layer of the DCNNs are used to train the Support Vector Machine (SVM). The integration of deep features with SVM enhanced the detection accuracy across all the DCNN-dataset combinations, according to analysis in terms of accuracy, precision, recall, and F1-score.

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“…Dataset ini berisi citra udara beresolusi tinggi beserta anotasi yang menunjukkan lokasi bangunan. Kita dapat menggunakan alat seperti LabelImg [28] untuk membuat anotasi untuk kumpulan data kita sendiri jika diperlukan. Jenis citra udara yang digunakan untuk artikel ini tentang deteksi bangunan atap menggunakan algoritme pembelajaran mendalam Yolov7 tidak ditentukan.…”

Section: Hasil Penelitian Dan Pembahasan 31 Dataset Citra Udaraunclassified

Deteksi Atap Bangunan Berbasis Citra Udara Menggunakan Google Colab dan Algoritma Deep Learning YOLOv7

Guntara

2023

JMASIF

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This article presents a deep learning-based approach for roof building detection in aerial imagery using the Yolov7 algorithm and Google Colab. We trained the Yolov7 model on a dataset of annotated aerial images and tested its performance on a testing set of 200 images. Our experimental results show that the Yolov7 model achieved an F1 score of 80% for roof building detection on the testing set, indicating that our approach can accurately detect roof buildings in aerial imagery. This approach can be useful in various applications, such as urban planning, disaster management, and infrastructure development. The availability of aerial imagery and the ability to use deep learning algorithms make it possible to automate the process of roof building detection, which can save time and resources. This research can be extended by investigating the use of other deep learning algorithms or combining multiple algorithms to improve the accuracy of roof building detection in aerial imagery.

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Deep Learning based Detection of potholes in Indian roads using YOLO

Cited by 48 publications

References 13 publications

A Pothole Can Be Seen with Two Eyes: An Ensemble Approach to Pothole Detection

A Pothole Can Be Seen with Two Eyes: An Ensemble Approach to Pothole Detection

Transfer learned deep feature based crack detection using support vector machine: a comparative study

Deteksi Atap Bangunan Berbasis Citra Udara Menggunakan Google Colab dan Algoritma Deep Learning YOLOv7

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