Detection of Road Damages Using Machine Learning Methods with Data Collected from Various Geographies: A Study on Türkiye

Ahmet Cihangir Kavcı; Ömer Faruk Cansız

Makale detayları

Detection of Road Damages Using Machine Learning Methods with Data Collected from Various Geographies: A Study on Türkiye

Yazarlar:

Ahmet Cihangir Kavcı | Ömer Faruk Cansız

Yayıncı:

Fırat University

Ana Başlıklar:

Ulaştırma Mühendisliği

Dergi:

Firat University Journal of Experimental and Computational Engineering Vol.3 Issue 3, pp.255-270

Belge Tipi:

Makale

Yayın Tarihi:

10/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001330482

pISSN:

2822-2881

Anahtar kelimeler:

Özet:

 Road damage seriously affects the comfort and safety of drivers. The detection of road damage is of great importance not only for transportation safety, but also in terms of cost. The detection of road damage is critical for enabling early intervention and repair. In this study, the road damage detection performance of the YOLO You Only Look Once v8 algorithm was evaluated using datasets obtained from different geographies, including Czechia -Türkiye, India-Türkiye, USA-Türkiye, and Japan-Türkiye. The findings revealed both the capabilities of the algorithm in damage detection and the challenges it faced in distinguishing certain types of damage. For the creation of the Türkiye dataset, images of roads in the province of Hatay were recorded. These images were labeled using Microsofts VoTT application. Comparisons and evaluations were made among the developed models. Among these models, the Japan-Türkiye model yielded the best results with a 0.55 mAP and 0.54 F1 score. The results of the models indicated that the appearance of damage varies according to the geographical location and the quality of road data. It was observed that data consisting of local images and uncertain damage types were important in training.

 Road damage seriously affects the comfort and safety of drivers. The detection of road damage is of great importance not only for transportation safety, but also in terms of cost. The detection of road damage is critical for enabling early intervention and repair. In this study, the road damage detection performance of the YOLO You Only Look Once v8 algorithm was evaluated using datasets obtained from different geographies, including Czechia -Türkiye, India-Türkiye, USA-Türkiye, and Japan-Türkiye. The findings revealed both the capabilities of the algorithm in damage detection and the challenges it faced in distinguishing certain types of damage. For the creation of the Türkiye dataset, images of roads in the province of Hatay were recorded. These images were labeled using Microsofts VoTT application. Comparisons and evaluations were made among the developed models. Among these models, the Japan-Türkiye model yielded the best results with a 0.55 mAP and 0.54 F1 score. The results of the models indicated that the appearance of damage varies according to the geographical location and the quality of road data. It was observed that data consisting of local images and uncertain damage types were important in training.

 Road damage seriously affects the comfort and safety of drivers. The detection of road damage is of great importance not only for transportation safety, but also in terms of cost. The detection of road damage is critical for enabling early intervention and repair. In this study, the road damage detection performance of the YOLO You Only Look Once v8 algorithm was evaluated using datasets obtained from different geographies, including Czechia -Türkiye, India-Türkiye, USA-Türkiye, and Japan-Türkiye. The findings revealed both the capabilities of the algorithm in damage detection and the challenges it faced in distinguishing certain types of damage. For the creation of the Türkiye dataset, images of roads in the province of Hatay were recorded. These images were labeled using Microsofts VoTT application. Comparisons and evaluations were made among the developed models. Among these models, the Japan-Türkiye model yielded the best results with a 0.55 mAP and 0.54 F1 score. The results of the models indicated that the appearance of damage varies according to the geographical location and the quality of road data. It was observed that data consisting of local images and uncertain damage types were important in training.

 Karayolu hasarı, özellikle sürücülerin konforunu ve güvenliğini ciddi şekilde etkilemektedir. Yollardaki hasarların tespiti, sadece ulaşım güvenliği açısından değil, aynı zamanda maliyet açısından da büyük önem taşımaktadır. Yol hasarlarının tespiti, erken müdahale ve onarımı sağlamak açısından kritik öneme sahiptir. Bu çalışmada, YOLO You Only Look Once v8 algoritmasının yol hasar tespit performansı, Çekya-Türkiye, Hindistan-Türkiye, ABD-Türkiye ve Japonya-Türkiye dahil olmak üzere farklı coğrafyalardan elde edilen veri setleri kullanılarak değerlendirildi. Bulgular, algoritmanın hasar tespit konusundaki yeteneklerini ve belirli hasar türlerini ayırt etmede karşılaştığı zorlukları ortaya koydu. Türkiye veri setinin oluşturulması için Hatay ilindeki yolların görüntüleri kaydedildi. Bu görüntüler, Microsoftun VoTT uygulaması kullanılarak etiketlendi. Geliştirilen modeller arasında karşılaştırmalar ve değerlendirmeler yapıldı. Bu modeller arasında en iyi sonuçları Japonya-Türkiye modeli, 0.55 mAP ve 0.54 F1 skoru ile verdi. Modellerin sonuçları, hasarın görünümünün coğrafi konuma ve yol verilerinin kalitesine göre değiştiğini gösterdi. Yerel görüntülerden ve belirsiz hasar türlerinden oluşan verilerin eğitimde önemli olduğu gözlemlendi.

Makale detayları

Detection of Road Damages Using Machine Learning Methods with Data Collected from Various Geographies: A Study on Türkiye

Yazarlar:

Ahmet Cihangir Kavcı | Ömer Faruk Cansız

Yayıncı:

Fırat University

Ana Başlıklar:

Ulaştırma Mühendisliği

Dergi:

Firat University Journal of Experimental and Computational Engineering Vol.3 Issue 3, pp.255-270

Belge Tipi:

Makale

Yayın Tarihi:

10/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001330482

pISSN:

2822-2881

Road damage detection

YOLO algorithm

Machine learning

Object detection

RDD2022Yol hasar tespiti

YOLO algoritması

Makine öğrenmesi

Nesne tespiti

RDD2022

Sayı

Makale detayları

Yazarlar: Ahmet Cihangir Kavcı | Ömer Faruk Cansız

Yayıncı: Fırat University

Ana Başlıklar: Ulaştırma Mühendisliği

Dergi: Firat University Journal of Experimental and Computational Engineering Vol.3 Issue 3, pp.255-270

Belge Tipi: Makale

Yayın Tarihi: 10/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001330482

pISSN: 2822-2881

Sayı

Yazarlar:

Ahmet Cihangir Kavcı | Ömer Faruk Cansız

Yayıncı:

Fırat University

Ana Başlıklar:

Ulaştırma Mühendisliği

Dergi:

Firat University Journal of Experimental and Computational Engineering Vol.3 Issue 3, pp.255-270

Belge Tipi:

Makale

Yayın Tarihi:

10/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001330482

pISSN:

2822-2881