Abstract
Attributable to global economic development and rapid urbanization, traffic explosion has been observed. This situation becomes very prominent as the traditional traffic control systems are incapable to efficiently monitor and control the traffic. Therefore, this paper presents automatic vehicle detection from satellite images using deep learning approaches. For this purpose, two most renowned and widely used detection algorithms (YOLOv4 and YOLOv3) have been employed to develop satellite image vehicle detector using publicly available DOTA dataset. This work confirms the supremacy of YOLOv4 over YOLOv3 by large improvements in mAP, IOU, precision, recall, F1-score with increase of 45%, 20%, 11.1%, 45.9%, and 29.5%, respectively. Therefore, these investigational results verify the strength of YOLOv4 algorithm for satellite image vehicle detection and recommend its use for the development of an intelligent traffic control system.
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The first and second authors would like to thank the Ministry of Human Resource Development, New Delhi, India, for providing the Research Fellowship for carrying out this work.
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Gupta, H., Jindal, P., Verma, O.P. (2021). Automatic Vehicle Detection from Satellite Images Using Deep Learning Algorithm. In: Sharma, T.K., Ahn, C.W., Verma, O.P., Panigrahi, B.K. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 1381. Springer, Singapore. https://doi.org/10.1007/978-981-16-1696-9_52
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DOI: https://doi.org/10.1007/978-981-16-1696-9_52
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