Vehicle Detection from Aerial Imagery Using Principal Component Analysis and Deep Learning

Kumar, C. Santhosh; Maharana, Advaith Das; Krishnan, Srinath Murali; Hanuma, Sannidhi Sri Sai; Sowmya, V.

doi:10.1007/978-3-031-27499-2_12

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2024

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Cited by 2 publications

References 18 publications

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Strategic Mobility Insights: Optimizing Parking and Operational Efficiency through Density Analysis

Praharsh,

Tarun,

Priya

et al. 2024

2024 International Conference on Intelligent Systems for Cybersecurity (ISCS)

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Strategic Mobility Insights: Optimizing Parking and Operational Efficiency through Density Analysis

Praharsh,

Tarun,

Priya

et al. 2024

2024 International Conference on Intelligent Systems for Cybersecurity (ISCS)

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FlightScope: An Experimental Comparative Review of Aircraft Detection Algorithms in Satellite Imagery

El Ghazouali,

Gucciardi,

Venturini

et al. 2024

Remote Sensing

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Object detection in remotely sensed satellite pictures is fundamental in many fields such as biophysical and environmental monitoring. While deep learning algorithms are constantly evolving, they have been mostly implemented and tested on popular ground-taken photos. This paper critically evaluates and compares a suite of advanced object detection algorithms customized for the task of identifying aircraft within satellite imagery. The goal is to enable researchers to choose efficiently from algorithms that are trainable and usable in real time on a deep learning infrastructure with moderate requirements. Using the large HRPlanesV2 dataset, together with rigorous validation with the GDIT dataset, this research encompasses an array of methodologies including YOLO versions 5, 8, and 10, Faster RCNN, CenterNet, RetinaNet, RTMDet, DETR, and grounding DINO, all trained from scratch. This exhaustive training and validation study reveals YOLOv5 as the pre-eminent model for the specific case of identifying airplanes from remote sensing data, showcasing high precision and adaptability across diverse imaging conditions. This research highlights the nuanced performance landscapes of these algorithms, with YOLOv5 emerging as a robust solution for aerial object detection, underlining its importance through superior mean average precision, recall, and intersection over union scores. The findings described here underscore the fundamental role of algorithm selection aligned with the specific demands of satellite imagery analysis and extend a comprehensive framework to evaluate model efficacy. This aims to foster exploration and innovation in the realm of remote sensing object detection, paving the way for improved satellite imagery applications.

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Vehicle Detection from Aerial Imagery Using Principal Component Analysis and Deep Learning

Cited by 2 publications

References 18 publications

Strategic Mobility Insights: Optimizing Parking and Operational Efficiency through Density Analysis

Strategic Mobility Insights: Optimizing Parking and Operational Efficiency through Density Analysis

FlightScope: An Experimental Comparative Review of Aircraft Detection Algorithms in Satellite Imagery

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