RAILWAY SECURITY SYSTEM DESIGN BY IMAGE PROCESSING AND DEEP LEARNING UNMANNED AERIAL VEHICLE

Yıl 2022, Cilt: 11 Sayı: 3, 150 - 154, 29.09.2022

Muzaffer Eylence , Mehmet Yücel , Mustafa Melikşah Özmen , Bekir Aksoy

https://doi.org/10.46810/tdfd.1112957

Cited By: 1

Öz

With the developing technology, technological blessings make human life easier and help them every day. Unmanned aerial vehicles (UAV), which is one of the technological blessings, have shown themselves in many fields, especially in fields such as the military, defense industry, photography, and hobby. With the development of defense systems with UAVs, the security of railways has also been left to UAVs. In this study, while the foreign matter separation is made on the railway by using the deep learning model in real-time, the image taken on the UAV is simultaneously controlled by using the image processing method. The fact that the deep learning model has a 0.99 mAP rate increases the reliability of the model.

Anahtar Kelimeler

Deep Learning, Image Processing, Unmanned Aerial Vehicle

Kaynakça

• [Sonay GÖRGÜLÜ BALCI. Hafif raylı sistemlerde lazerli engel algılayıcı sistem tasarımı [dissertation]. Kırıkkale üniversitesi; 2014.
• Feng H, Jiang Z, Xie F, Yang P, Shi J, Chen L. Automatic Fastener Classification and Defect Detection in Vision-Based Railway Inspection Systems. IEEE Transactions on Instrumentation and Measurement. 2014;63(4):877-888.
• Shah A, Bhatti N, Dev K, Chowdhry B. MUHAFIZ: IoT-Based Track Recording Vehicle for the Damage Analysis of the Railway Track. IEEE Internet of Things Journal. 2021;8(11):9397-9406.
• Aydin I, Sevi M, Sahbaz K, Karakose M. Detection of Rail Defects with Deep Learning Controlled Autonomous UAV. Sakheer, Bahrain: Sakheer, Bahrain; 2021.
• KAYA V, BARAN A, TUNCER S. Dinamit Destekli Terör Faaliyetlerinin Önlenmesi İçin Derin Öğrenme Temelli Güvenlik Destek Sistemi. European Journal of Science and Technology. 2021;.
• Shafique R, Siddiqui H, Rustam F, Ullah S, Siddique M, Lee E et al. A Novel Approach to Railway Track Faults Detection Using Acoustic Analysis. Sensors. 2021;21(18):6221.
• AYDIN İ, GÜÇLÜ E, AKIN E. Mask R-CNN Algoritmasını Kullanarak Demiryolu Travers Eksikliklerinin Tespiti İçin Otonom İHA Tasarımı. Fırat Üniversitesi Mühendislik Bilimleri Dergisi. 2022;.
• Zhang H, Jin X, Wu Q, Wang Y, He Z, Yang Y. Automatic Visual Detection System of Railway Surface Defects With Curvature Filter and Improved Gaussian Mixture Model. IEEE Transactions on Instrumentation and Measurement. 2018;67(7):1593-1608.
• Singh M, Singh S, Jaiswal J, Hempshall J. Autonomous Rail Track Inspection using Vision Based System. 2006 IEEE International Conference on Computational Intelligence for Homeland Security and Personal Safety. 2006;.
• Faghih-Roohi S, Hajizadeh S, Nunez A, Babuska R, De Schutter B. Deep convolutional neural networks for detection of rail surface defects. 2016 International Joint Conference on Neural Networks (IJCNN). 2016;.
• Bayhan E, Ozkan Z, Namdar M, Basgumus A. Deep Learning Based Object Detection and Recognition of Unmanned Aerial Vehicles. 2021 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA). 2021;.
• Yan B, Fan P, Lei X, Liu Z, Yang F. A Real-Time Apple Targets Detection Method for Picking Robot Based on Improved YOLOv5. Remote Sensing. 2021;13(9):1619.
• Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition 2016. p. 779-788.
• GitHub - ultralytics/yolov5: YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite [Internet]. GitHub. 2022 [cited 20 Apr. 2022]. Available from: https://github.com/ultralytics/yolov5
• Murat S. İNSANSIZ HAVA ARACI GÖRÜNTÜLERİNDEN DERİN ÖĞRENME YÖNTEMLERİYLE NESNE TANIMA [YL]. Maltepe Üniversitesi; 2021.
• Wang Z, Wu L, Li T, Shi P. A Smoke Detection Model Based on Improved YOLOv5. Mathematics. 2022;10(7):1190.
• BULUT F. DEĞİŞTİRİLMİŞ AYRIK HAAR DALGACIK DÖNÜŞÜMÜ İLE YENİ BİR HİSTOGRAM EŞİTLEME YÖNTEMİ. Mühendislik Bilimleri ve Tasarım Dergisi. 2022;10(1):188-200..
• Jebadass J, Balasubramaniam P. Low light enhancement algorithm for color images using intuitionistic fuzzy sets with histogram equalization. Multimedia Tools and Applications. 2022;81(6):8093-8106.
• Cartucho J, Ventura R, Veloso M. Robust Object Recognition Through Symbiotic Deep Learning In Mobile Robots. 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 2018;.
• Qi J, Liu X, Liu K, Xu F, Guo H, Tian X et al. An improved YOLOv5 model based on visual attention mechanism: Application to recognition of tomato virus disease. Computers and Electronics in Agriculture. 2022;194:106780.