Year: 2025
Type: conference-paper
Abstract: This paper presents a robust approach for object detection in aerial imagery using the YOLOv5 model. We focus on identifying critical objects such as ambulances, car crashes, police vehicles, tow trucks, fire engines, overturned cars, and vehicles on fire. By leveraging a custom dataset, we outline the complete pipeline from data collection and annotation to model training and evaluation. Our results demonstrate that YOLOv5 achieves [email protected] of 81.7% and [email protected]:0.95 of 62.7%, achieving an optimal balance between speed (30 FPS) and accuracy. This work addresses key challenges in aerial imagery, including small object detection and complex backgrounds, and provides insights for future research in automated emergency response systems.
Source: Proceedings of IEEE Southeastcon
Authors: Sindhu Boddu, Arindam Mukherjee, Manan Agarwal
Institution: University of North Carolina at Charlotte
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4409796346