Farklı Coğrafyalardan Elde Edilen Verilerle Yol Hasarlarının Makine Öğrenmesi Yöntemleri Kullanılarak Tespiti: Türkiye Üzerine Bir İnceleme

Öz

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, Microsoft'un 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.

Anahtar Kelimeler

• Yol hasar tespiti
• YOLO algoritması
• Makine öğrenmesi
• Nesne tespiti
• RDD2022

Proje Numarası

2022LTP06

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

Öz

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 Microsoft's 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.

Anahtar Kelimeler

• Road damage detection
• YOLO algorithm
• Machine learning
• Object detection
• RDD2022

Destekleyen Kurum

Scientific Research Projects Coordination Office at İskenderun Technical University

Proje Numarası

2022LTP06

Etik Beyan

There is no need to obtain ethics committee permission for the prepared article.

Teşekkür

This investigation was conducted with the assistance of the Scientific Research Projects Coordination Office at İskenderun Technical University, within the framework of project number 2022LTP06.

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