Survey on Detection Methods for Self-Driving Cars


  • Myat Su Oo Digital Image Processing, University of Computer Studies Mandalay, Myanmar
  • Dr. May ` The` Yu Faculty of Information Science, University of Computer Studies Mandalay, Myanmar




Accurate vehicle detection or classification plays an important role for self-driving cars. Objects classification and detection can be used in various such as Robotics, Medical Diagnosis, Safety, Industrial Inspection and Automation, Human Computer Interface, Advanced Driver Assistance System and Information Retrieval. In this article, we investigated the methods of detection and classification in context images and videos. SIFT, HOG, SVM, CNN, faster RCNN and YOLO methods are reviewed to detect and recognize the objects. The paper aims to know the methods that detect the obstacles on the way to reduce the traffic accidents. We summarize the results, faster-RCNN is better than the other methods for real-time citing the advantages and disadvantages of existing methods.


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How to Cite

Su Oo, M., & The` Yu, D. M. `. (2019). Survey on Detection Methods for Self-Driving Cars. International Journal of Computer (IJC), 32(1), 73–82. Retrieved from