Cep Telefonu Kullanılarak Araç Hızı ile Çukur Tespiti Arasındaki İlişkinin Araştırılması

Year 2024, Volume: 24 Issue: 1, 228 - 241, 27.02.2024

Ömer Kaya , Muhammed Yasin Çodur

https://doi.org/10.35414/akufemubid.1328778

Abstract

Yol kaplamalarının zaman, iklim koşulları ve inşaat hatalarından dolayı bozulduğu bilinmektedir. Bu hasarlar dikkate alındığında yol güvenliğini ve konforunu azaltan en önemli yol kusurlarından biri çukurlardır. Özellikle çukurun genişliği ve derinliği arttıkça sürüş güvenliğini de tehlikeye atmaktadır. Özellikle şehir içi yollarda bu çukurların konumları birçok bölgede manuel olarak belirlenmektedir. Bu süreç çukurların bakım ve onarımında gecikmelere neden olmaktadır. Bu amaçla yazarlar, yol ağında meydana gelen çukurları otomatik olarak tespit etmek için birden fazla aşamadan oluşan araç içi entegre bir sistem planlıyorlar. Bu sistemin ilk aşaması, yüksek doğrulukta nesne algılama yöntemleri ile çukurların belirlenmesidir. Ancak bu sistemde araç hızının çukur tespiti üzerindeki etkisi bilinmemektedir. Bu karmaşık durumu çözmek için aynı yol ve çukur üzerinde farklı araç hızlarında gerçek zamanlı video kayıtları yapılmıştır. Daha sonra YOLOv7 ve YOLOv8 tek aşamalı detektör ile bu videolar üzerinden çukur tespit işlemi gerçekleştirilmiştir. Elde edilen sonuçlar incelendiğinde araç hızı ile çukur tespiti arasında kesin bir ilişki tespit edilememiştir. Bu durum kamera açısı, görüntü kalitesi, güneş ışığı durumu gibi çeşitli parametrelere göre değişiklik gösterebilmektedir. Ayrıca her iki model performans kriterlerine göre karşılaştırıldığında YOLOv7'nin mAP0.5, hassasiyet, geri çağırma ve F1 skoru değerlerinde YOLOv8'e kısmi üstünlüğü bulunmaktadır. Bu kriterlerin 1'e yakın olması anlamlıdır. Son olarak videodan elde edilen görsellerden elde edilen algılama sonuçları, modellerde aşırı uyumun olmadığını göstermiştir.

Keywords

Yol Hasarı, Çukur, Otomatik Algılama, Hız, Akıllı Ulaşım Sistemleri

Investigating The Relationship Between Vehicle Speed and Pothole Detection by Using Mobile Phone

Abstract

It is known that road pavements are damaged due to time, climatic conditions and construction errors. Considering these damages, the most important road defect that reduces road safety and comfort is potholes. Especially as the width and depth of the pothole increases, driving safety is also endangered. In addition, the locations of these potholes, especially on urban roads, are determined manually in many regions. This process causes delays in the maintenance and repair of the potholes. To this end, the authors plan an in-vehicle integrated system consisting of multiple stages to automatically detect potholes occurring in the road network. The main purpose of the planned system is to identify potholes with high accuracy. However, the effect of vehicle speed on pothole detection in this system is unknown. In order to solve this complex situation, real-time video recordings were made on the same road and pothole at different vehicle speeds. Then, the pothole detection process was realized through these videos with the single-stage detector YOLOv7 vs YOLOv8. When the results obtained were examined, exact relationship could not be determined between vehicle speed and pothole detection. This situation may vary according to various parameters such as camera angle, image quality, sunlight condition. In addition, when both models are compared according to the performance criteria, YOLOv7 has a partial superiority over YOLOv8 in mAP0.5, precision, recall and F1 score values. It is especially significant that these criteria are close to 1. Finally, the perception results obtained from the images obtained from the video showed that there was no overfitting in the models.

Keywords

Road damage, Pothole, Automatic Detection, Speed, Intelligent Transportation Systems

References

• Anaissi, A., Khoa, N.L.D., Rakotoarivelo, T., Alamdari, M.M., Wang, Y., 2019. Smart pothole detection system using vehicle-mounted sensors and machine learning. Journal of Civil Structural Health Monitoring, 9, 91–102. https://doi.org/10.1007/s13349-019-00323-0
• Arya, D., Maeda, H., Ghosh, S.K., Toshniwal, D., Mraz, A., Kashiyama, T., Sekimoto, Y., 2021. Deep learning-based road damage detection and classification for multiple countries. Automation in Construction, 132, 103935. https://doi.org/https://doi.org/10.1016/j.autcon.2021.103935
• Aşcı, G., Karslıgil, M.E., 2020. Road Damage Detection via in Car Cameras. 28th Signal Processing and Communications Applications Conference (SIU). Gaziantep, Türkiye. 1–4. https://doi.org/10.1109/SIU49456.2020.9302086
• Cao, M.T., Tran, Q.V., Nguyen, N.M., Chang, K.T., 2020. Survey on performance of deep learning models for detecting road damages using multiple dashcam image resources. Advanced Engineering Informatics, 46, 101182. https://doi.org/https://doi.org/10.1016/j.aei.2020.101182
• Doshi, K., Yilmaz, Y., 2020. Road Damage Detection using Deep Ensemble Learning. IEEE International Conference on Big Data. Atlanta, USA, 5540–5544. https://doi.org/10.1109/BigData50022.2020.9377774
• Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A., 2010. The pascal visual object classes (VOC) challenge. International Journal of Computer Vision, 88, 303–338. https://doi.org/10.1007/s11263-009-0275-4
• Gong, L., An, L., Liu, M., Zhang, J., 2012. Road damage detection from high-resolution RS image. IEEE International Geoscience and Remote Sensing Symposium. Münich, Germany, 990–993. https://doi.org/10.1109/IGARSS.2012.6351235
• Guo, L., Li, R., Jiang, B., 2021. A road surface damage detection method using yolov4 with pid optimizer. International Journal of Innovative Computing, Information and Control, 17, 1763–1774. https://doi.org/10.24507/ijicic.17.05.1763
• Hoang Ngan Le, T., Zheng, Y., Zhu, C., Luu, K., Savvides, M., 2016. Multiple Scale Faster-RCNN Approach to Driver’s Cell-Phone Usage and Hands on Steering Wheel Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. Las Vegas, USA, 46–53. https://doi.org/ 10.1109/CVPRW.2016.13
• Huang, T.-W., Su, G.-M., 2021. Revertible Guidance Image Based Image Detail Enhancement. IEEE International Conference on Image Processing (ICIP). Anchorage, USA, 1704–1708. https://doi.org/10.1109/ICIP42928.2021.9506374
• Hussain, M., Al-Aqrabi, H., Munawar, M., Hill, R., Alsboui, T., 2022. Domain Feature Mapping with YOLOv7 for Automated Edge-Based Pallet Racking Inspections. Sensors 22. https://doi.org/10.3390/s22186927
• Jeong, D., 2020. Road Damage Detection Using YOLO with Smartphone Images. IEEE International Conference on Big Data. Atlanta, USA, 5559–5562. https://doi.org/10.1109/BigData50022.2020.9377847
• Kim, M., Jeong, J., Kim, S., 2021. Ecap-yolo: Efficient channel attention pyramid yolo for small object detection in aerial image. Remote Sensing, 13, 1–20. https://doi.org/10.3390/rs13234851
• Krizhevsky, A., Hinton, G.E., 2012. ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems, 60, 6, 84-90. https://doi.org/10.1145/3065386
• Lee, T., Chun, C., Ryu, S.K., 2021. Detection of road-surface anomalies using a smartphone camera and accelerometer. Sensors, 21, 1–17. https://doi.org/10.3390/s21020561
• Ma, H., Lu, N., Ge, L., Li, Q., You, X., Li, X., 2013. Automatic road damage detection using high-resolution satellite images and road maps. IEEE International Geoscience and Remote Sensing Symposium - IGARSS. Melbourne, Australia, 3718–3721. https://doi.org/10.1109/IGARSS.2013.6723638
• Maeda, H., Kashiyama, T., Sekimoto, Y., Seto, T., Omata, H., 2021. Generative adversarial network for road damage detection. Computer-Aided Civil and Infrastructure Engineering, 36, 47–60. https://doi.org/10.1111/mice.12561
• Maeda, H., Sekimoto, Y., Seto, T., Kashiyama, T., Omata, H., 2018. Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images. Computer-Aided Civil and Infrastructure Engineering, 33, 12, 1127–1141. https://doi.org/10.1111/mice.12387
• Mandal, V., Mussah, A.R., Adu-Gyamfi, Y., 2020. Deep Learning Frameworks for Pavement Distress Classification: A Comparative Analysis, IEEE International Conference on Big Data (Big Data). Atlanta, USA, 5577–5583. https://doi.org/10.1109/BigData50022.2020.9378047
• Patel, K., Bhatt, C., Mazzeo, P.L., 2022. Improved Ship Detection Algorithm from Satellite Images Using YOLOv7 and Graph Neural Network. Algorithms, 15, 12. https://doi.org/10.3390/a15120473
• Pi, A., Nath, N., Sampathkumar, S., Behzadan, A., 2021. Deep Learning for Visual Analytics of the Spread of COVID-19 Infection in Crowded Urban Environments. Natural Hazards Review, 22, 3. https://doi.org/10.1061/(ASCE)NH.1527-6996.0000492
• Redmon, J., Divvala, S., Girshick, R., Farhadi, A., 2016. You Only Look Once : Unified , Real-Time Object Detection, IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, USA, 779-788. https://doi.org/10.1109/CVPR.2016.91
• Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L., 2015. ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision, 115, 211–252. https://doi.org/10.1007/s11263-015-0816-y
• Sultana, F., Sufian, A., Dutta, P., 2019. A review of object detection models based on convolutional neural network. Computer Vision and Pattern Recognition, 1157, 1-16. https://doi.org/https://doi.org/10.48550/arXiv.1905.01614
• Tsung-Yi, L., Maire, M., Belongie, S., Hays, J., Pietro, P., Ramanan, D., Dollar, Piotr, Zitnick, L., 2014. Microsoft COCO: Common Objects in Context. Computer Vision – ECCV 2014. 8693, 740–755. https://doi.org/https://doi.org/10.1007/978-3-319-10602-1_48
• Wang, C., Bochkovskiy, A., Liao, H.M., 2022. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. Computer Vision and Pattern Recognition, 1–15. https://doi.org/https://doi.org/10.48550/arXiv.2207.02696
• Wu, C., Ye, M., Zhang, J., Ma, Y., 2023. YOLO-LWNet : A Lightweight Road Damage Object Detection Network for Mobile Terminal Devices. Sensors, 23,6. https://doi.org/10.3390/s23063268
• Wu, D., Jiang, S., Zhao, E., Liu, Y., Zhu, H., Wang, W., Wang, R., 2022. Detection of Camellia oleifera Fruit in Complex Scenes by Using YOLOv7 and Data Augmentation. Applied Sciences, 12, 22. https://doi.org/10.3390/app122211318
• Yin, J., Qu, J., Huang, W., Chen, Q., 2021. Road damage detection and classification based on multi-level feature pyramids. KSII Transactions on Internet and Information Systems, 15, 2, 786–799. https://doi.org/10.3837/tiis.2021.02.022
• Zaidi, S.S.A., Ansari, M.S., Aslam, A., Kanwal, N., Asghar, M., Lee, B., 2022. A survey of modern deep learning based object detection models. Digital Signal Processing: A Review Journal, 126, 103514. https://doi.org/10.1016/j.dsp.2022.103514
• Ritchie, H., Roser, M., Age Structure, https://ourworldindata.org/age-structure, (15.01.2023).
• Batchelor, T., 50 cyclists killed or seriously injured every year because of Britain’s poor roads, https://www.independent.co.uk/news/uk/home-news/cyclist-road-deaths-injuries-pothole-statistics-chris-boardman-department-transport-a7535816.html, (12.10.2022).
• Dash, D., Potholes killed 3,597 across India in 2017, terror 803,https://timesofindia.indiatimes.com/india/potholes-killed-3597-across-india-in-2017-terror-803/articleshow/64992956.cms, (12.06.2022).
• Traffic / Speed Limits. https://www.kgm.gov.tr/sayfalar/kgm/sitetr/trafik/hizsinirlari.aspx, (16.06.2023).
• Global Road Damage Detection, https://rdd2020.sekilab.global/ (13.01.2022).
• Francesco, J.S., What is YOLOv8? The Ultimate Guide, https://blog.roboflow.com/whats-new-in-yolov8/, (20.01.2023).
• YOLOv8, https://github.com/ultralytics/ultralytics , (12.01.2023).
• Road damage detector, https://github.com/sekilab/RoadDamageDetector , (19.03.2023)
• Give your software the sense of sight, https://roboflow.com/, (16.02.2023)