Budak, S.Bozkaya, F.Akmaz, M.Y.Tiglioglu, S.Boynukara, C.Kazancı, O.Budak, Z.H.Y.2023-10-022023-10-0220239798350321388https://doi.org/10.1109/ECAI58194.2023.10193941https://hdl.handle.net/20.500.13091/4629Orange TM;STC15th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2023 -- 29 June 2023 through 30 June 2023 -- 191300Autonomous vehicles use many technologies and methods to detect and act on surrounding objects. The most common among these technologies is an algorithm called YOLO (You Only Look Once). This algorithm quickly detects objects in an image and classifies these objects accurately. This study examines the use of the YOLO algorithm for signage detection in autonomous vehicles and how this algorithm can be improved. First of all, the basic principles and working mechanisms of the YOLO algorithm are explained. Then, it is explained in detail how this algorithm can be used for plate detection in autonomous vehicles. Various models were trained using the YOLO algorithm and the data set created with real data, and the trained models were tested on real-time systems. Finally, suggestions for the improvement of the YOLO algorithm are presented and how this algorithm can be improved further in the future is discussed. © 2023 IEEE.eninfo:eu-repo/semantics/closedAccessAutonomous vehicles; Data replication; Enhancement automatic feature recognition; Image processing; Plate detection; YOLO algorithmImage enhancement; Interactive computer systems; Object detection; Real time systems; Traffic signs; Automatic feature recognition; Autonomous Vehicles; Data replication; Enhancement automatic feature recognition; Images processing; Plate detections; Replication method; Traffic sign detection; Vehicle use; You only look once algorithm; Autonomous vehiclesConference Object10.1109/ECAI58194.2023.101939412-s2.0-85168110061