A Convolutional Neural Network Approach for Road Anomalies Detection in Bangladesh with Image Thresholding

Ishtiak, Taoseef; Ahmed, Sajid; Anila, M; Farah, Tanjila

doi:10.1109/worlds4.2019.8903936

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…Bahkan, algoritma tersebut juga digunakan untuk mengidentifikasi tingkat kerusakan jalan dan membantu dalam perencanaan perawatan jalan yang efektif [8][9][10]. Klasifikasi kerusakan jalan menggunakan pembelajaran mendalam mampu memberikan hasil yang baik dalam beberapa studi [11][12][13]. Pada penelitian sebelumnya, penerapan kedua pendekatan tersebut mampu mengklasifikasikan objek pada citra satelit dengan akurasi hingga 93,49% [14].…”

Section: Pendahuluanunclassified

Klasifikasi Kerusakan Jalan Raya Berbasis Citra Udara Menggunakan Object-Based Image-Analysis (Obia)

Sukmaningsih

2024

Prosiding Seminar Nasional Fisika (E-Journal) snf2016 Unj

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Kerusakan jalan merupakan masalah serius yang dapat menyebabkan kemacetan, kecelakaan, dan risiko keselamatan. Pada makalah ini, diusulkan penggunaan citra drone untuk mengidentifikasi kerusakan jalan raya. Pendekatan pixel-to-pixel pada citra drone yang beresolusi spasial tinggi sulit dilakukan karena kerusakan jalan menyebar ke beberapa piksel. Oleh karena itu, pendekatan OBIA digunakan untuk mengklasifikasikan kerusakan jalan raya dengan mem fokuskan objek sebagai kesatuan. Klasifikasi citra dilakukan menggunakan pendekatan Object-Based Image-Analysis (OBIA) dengan mengimplementasikan algoritma Simple Linear Iterative Clustering (SLIC). SLIC akan dieksplorasi dengan memvariasikan jumlah cluster untuk mendapatkan metode ekstraksi ciri yang tepat sebelum dilakukan klasifikasi menggunakan CNN. Terdapat dua objek yang dipilih untuk memvalidasi hasil klasifikasi, yaitu jalan rusak dan jalan tidak rusak. Hasil eksperimen menunjukkan bahwa pendekatan OBIA mampu mengidentifikasi objek kerusakan jalan dengan optimal. Bahkan, pemilihan jumlah cluster juga mempengaruhi nilai akurasi klasifikasi. Penggunaan jumlah maksimal 300 cluster memberikan hasil akurasi klasifikasi terbaik dengan peningkatan 13,87% dibandingkan dengan 500 cluster.

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Section: Pendahuluanunclassified

Klasifikasi Kerusakan Jalan Raya Berbasis Citra Udara Menggunakan Object-Based Image-Analysis (Obia)

Sukmaningsih

2024

Prosiding Seminar Nasional Fisika (E-Journal) snf2016 Unj

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“…As a result, the output of unsupervised learning models is not predetermined, allowing computers to independently discern anomalies in the data through classification processes [ 95 ]. Ishtiak et al [ 43 ] proposed a system for identifying and categorising various road conditions, including visco-plastic deformities and defects. This approach uses a statistical analysis method and a scoring function considering several factors, such as road colour, material, and image quality.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The post-processing method is then applied to estimate the crack’s thickness and provide the complete crack pattern. Shankar and Wang [ 111 ] proposed a Fully Convolutional Neural Network (FCNN) model for anomaly detection, while Ishtiak and Ahmed [ 43 ] utilised a two-step image classification approach in their FCNN model. The first step involved feeding road surface images into the FCNN, with the model achieving 87% accuracy for all classes.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Identifying road defects and anomalies helps reduce drivers’ risks while supporting road maintenance [ 12 ]. ARDAD systems can play a significant role in augmenting visual surveillance to safeguard the public and private transportation of modern cities roads [ 41 ], sub-urban and rural roads [ 42 , 43 ], animal hazard-prone hinterlands such as wilderness roads [ 30 ], and avalanche-prone mountainous roads [ 44 ]. This systematic review is the first in which the authors summarise the performance and accuracy of hazard detection systems used in road infrastructure surveillance achieved globally.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automated Road Defect and Anomaly Detection for Traffic Safety: A Systematic Review

Rathee

Bačić

Doborjeh

2023

Sensors

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Recently, there has been a substantial increase in the development of sensor technology. As enabling factors, computer vision (CV) combined with sensor technology have made progress in applications intended to mitigate high rates of fatalities and the costs of traffic-related injuries. Although past surveys and applications of CV have focused on subareas of road hazards, there is yet to be one comprehensive and evidence-based systematic review that investigates CV applications for Automated Road Defect and Anomaly Detection (ARDAD). To present ARDAD’s state-of-the-art, this systematic review is focused on determining the research gaps, challenges, and future implications from selected papers (N = 116) between 2000 and 2023, relying primarily on Scopus and Litmaps services. The survey presents a selection of artefacts, including the most popular open-access datasets (D = 18), research and technology trends that with reported performance can help accelerate the application of rapidly advancing sensor technology in ARDAD and CV. The produced survey artefacts can assist the scientific community in further improving traffic conditions and safety.

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Road Condition Detection and Crowdsourced Data Collection for Accident Prevention: A Deep Learning Approach

Jahan,

Islam,

Hossain

et al. 2023

2023 Twelfth International Conference on Image Processing Theory, Tools and Applications (IPTA)

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A Convolutional Neural Network Approach for Road Anomalies Detection in Bangladesh with Image Thresholding

Cited by 5 publications

References 10 publications

Klasifikasi Kerusakan Jalan Raya Berbasis Citra Udara Menggunakan Object-Based Image-Analysis (Obia)

Klasifikasi Kerusakan Jalan Raya Berbasis Citra Udara Menggunakan Object-Based Image-Analysis (Obia)

Automated Road Defect and Anomaly Detection for Traffic Safety: A Systematic Review

Road Condition Detection and Crowdsourced Data Collection for Accident Prevention: A Deep Learning Approach

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