TY - GEN
T1 - Object Detection for Autonomous Vehicle using Single Camera with YOLOv4 and Mapping Algorithm
AU - Sahal, Mochammad
AU - Kurniawan, Ade Oktavianus
AU - Kadir, Rusdhianto Effendi Abdul
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - CNN
KW - Machine learning
KW - Object Detection
KW - autonomous vehicle
UR - http://www.scopus.com/inward/record.url?scp=85126647147&partnerID=8YFLogxK
U2 - 10.1109/ISRITI54043.2021.9702764
DO - 10.1109/ISRITI54043.2021.9702764
M3 - Conference contribution
AN - SCOPUS:85126647147
T3 - 2021 4th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2021
SP - 144
EP - 149
BT - 2021 4th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2021
Y2 - 16 December 2021
ER -