YOLO vs RCNN for Real Time Aerial Survey: A Review

Mudasir Ashraf

Abstract: This article reviews the effectiveness of two prominent object detection algorithms, YOLO (You Only Look Once) and RCNN (Region - based Convolutional Neural Networks), specifically in the context of real - time aerial surveys. YOLO is known for its speed and ability to process data in real - time, making it ideal for applications where rapid decision - making is required. In contrast, RCNN offers higher detection accuracy by using a multi - step process, which is particularly beneficial in tasks that demand precise object identification, though it requires more computational resources. This review explores the strengths and limitations of each algorithm to guide researchers and practitioners in selecting the most suitable approach for aerial data collection.

Keywords: aerial survey, YOLO, RCNN, real - time detection, object detection

How to Cite?: Mudasir Ashraf, "YOLO vs RCNN for Real Time Aerial Survey: A Review", Volume 13 Issue 11, November 2024, International Journal of Science and Research (IJSR), Pages: 545-549, https://www.ijsr.net/getabstract.php?paperid=SR241107120637, DOI: https://dx.doi.org/10.21275/SR241107120637