Yolo5-Based UAV Surveillance for Tiny Object Detection on Airport Runways

Sri Hari, R V; Ambalam, Rishywanth; Ruban Kumar, B; Ibrahim, M; Ponnusamy, R

doi:10.1109/icdsaai59313.2023.10452584

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…For example, Zhai et al proposed an automatic segmentation and enhancement method for airport pavements based on 3D images [3]. Ambalam et al developed an automated object detection framework by harnessing the capabilities of YOLOv5: the integration of Unmanned Aerial Vehicles (UAVs) into surveillance operations for the precise identification of Foreign Object Debris (FOD) on airport runways [4].…”

Section: Introductionmentioning

confidence: 99%

Next-Gen Remote Airport Maintenance: UAV-Guided Inspection and Maintenance Using Computer Vision

Yang,

Nashik,

Huang

et al. 2024

Drones

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This paper presents a novel system for the automated monitoring and maintenance of gravel runways in remote airports, particularly in Northern Canada, using Unmanned Aerial Vehicles (UAVs) and computer vision technologies. Due to the geographic isolation and harsh weather conditions, these airports face unique challenges in runway maintenance. Our approach integrates advanced deep learning algorithms and UAV technology to provide a cost-effective, efficient, and accurate means of detecting runway defects, such as water pooling, vegetation encroachment, and surface irregularities. We developed a hybrid approach combining the vision transformer model with image filtering and thresholding algorithms, applied on high-resolution UAV imagery. This system not only identifies various types of defects but also evaluates runway smoothness, contributing significantly to the safety and reliability of air transport in these areas. Our experiments, conducted across multiple remote airports, demonstrate the effectiveness of our approach in real-world scenarios, offering significant improvements over traditional manual inspection methods.

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Section: Introductionmentioning

confidence: 99%

Next-Gen Remote Airport Maintenance: UAV-Guided Inspection and Maintenance Using Computer Vision

Yang,

Nashik,

Huang

et al. 2024

Drones

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Real-Time Recognition Algorithm of Small Target for UAV Infrared Detection

Zhang,

Zhou,

2024

Sensors

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Unmanned Aerial Vehicle (UAV) infrared detection has problems such as weak and small targets, complex backgrounds, and poor real-time detection performance. It is difficult for general target detection algorithms to achieve the requirements of a high detection rate, low missed detection rate, and high real-time performance. In order to solve these problems, this paper proposes an improved small target detection method based on Picodet. First, to address the problem of poor real-time performance, an improved lightweight LCNet network was introduced as the backbone network for feature extraction. Secondly, in order to solve the problems of high false detection rate and missed detection rate due to weak targets, the Squeeze-and-Excitation module was added and the feature pyramid structure was improved. Experimental results obtained on the HIT-UAV public dataset show that the improved detection model’s real-time frame rate increased by 31 fps and the average accuracy (MAP) increased by 7%, which proves the effectiveness of this method for UAV infrared small target detection.

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Yolo5-Based UAV Surveillance for Tiny Object Detection on Airport Runways

Cited by 2 publications

References 8 publications

Next-Gen Remote Airport Maintenance: UAV-Guided Inspection and Maintenance Using Computer Vision

Next-Gen Remote Airport Maintenance: UAV-Guided Inspection and Maintenance Using Computer Vision

Real-Time Recognition Algorithm of Small Target for UAV Infrared Detection

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