Loading [a11y]/accessibility-menu.js
Performance Evaluation of YOLOv4 for Instant Object Detection in UAVs | IEEE Conference Publication | IEEE Xplore

Performance Evaluation of YOLOv4 for Instant Object Detection in UAVs


Abstract:

Unmanned Aerial Vehicles (UAVs) have become integral in various research domains due to the advantages they provide. Current UAV systems rely on Global Navigation Satelli...Show More

Abstract:

Unmanned Aerial Vehicles (UAVs) have become integral in various research domains due to the advantages they provide. Current UAV systems rely on Global Navigation Satellite Systems (GNSS) for flight control and sensors for obstacle detection, yet fully autonomous decision-making remains a challenge. This study evaluates the performance of YOLOv4, a Convolutional Neural Network (CNN) image recognition algorithm, for instantaneous object detection and classification in UAV-captured aerial images. The study demonstrates the applicability of YOLOv4 in real-time object detection and classification through UAV image feeds. The proposed approach advances the understanding of deploying CNNs in UAVs, offering a cost-effective solution for real-time object detection and classification, essential for autonomous UAV operations.
Date of Conference: 15-18 May 2024
Date Added to IEEE Xplore: 23 July 2024
ISBN Information:
Print on Demand(PoD) ISSN: 2165-0608
Conference Location: Mersin, Turkiye