Object Detection for Autonomous Vehicle using Single Camera with YOLOv4 and Mapping Algorithm

In this paper, we propose a new algorithm combined with the existing object recognition algorithm. Multi-object recognition algorithms are now various, with their respective advantages and disadvantages according to their uses. However, these algorithms can only detect and recognize objects without being able to know the location of the object relative to the sensor. The ability to know the location of the object is needed so that the autonomous car can make the right decisions without harming the driver. Since it requires fast and precise object detection and recognition capabilities, the algorithm used in object recognition is YOLOv4 with CSPDarknet-53. And because object recognition uses a neural network, the algorithm in determining the location of the object needs to be made as efficient as possible without affecting the performance of the object recognition algorithm, so that the mapping algorithm is used. The YOLOv4 model used has a precision value of 57.23 percent with a detection capability of 0.03785 seconds without a mapping algorithm, and if it is added with a mapping algorithm, the detection time becomes 0.03792 seconds. Since it has fast detection time, thus it can be applied to a real-time application.