Traffic Signs Detection System Based on You Only Look Once (Yolov8) using Raspberry Pi
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Abstract
Autonomous vehicle (AV) is projected to become a part of land transportation in the New Capital City of Nusantara (IKN). . This opens up opportunities for related research to be conducted prior to actual implementation.. One of the capabilities of AV is recognizing different types of traffic signs. Therefore, this study aims to design a traffic sign detection system as an insight and support for the implementation of autonomous vehicles in IKN Nusantara. To achieve this, a total of 11,157 images containing 30 types of traffic signs were collected as the primary dataset along the roads of the Special Region of Yogyakarta. Variations of the dataset were also added in the form of noise, blur, and dark. During the model training, hyper-parameter configurations such as learning rate, epoch, and image size were performed. In this study, the You Only Look Once v8 method is used. The results of testing with daytime data showed an accuracy of 80%, recall of 83%, and precision of 96%. In contrast, tests with night data showed 93% precision, 70% recall, and 67% accuracy. This test works well for cars moving at speeds below than 40 km/h because of the Raspberry Pi hardware's computing speed constraints
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