Real-Time Object Detection and Classification by UAV Equipped With SAR

Gromada, Krzysztof; Siemiątkowska, B.; Stecz, Wojciech; Płochocki, Krystian; Woźniak, Karol

doi:10.3390/s22052068

Cited by 15 publications

(6 citation statements)

References 29 publications

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“…The neural network would be able to focus on shadows and high objects for such a task. This can be advantageous, as shown in [26].…”

Section: Self-supervised Learning Techniquesmentioning

confidence: 99%

Unsupervised SAR Imagery Feature Learning with Median Filter-Based Loss Value

Gromada

2022

Sensors

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The scarcity of open SAR (Synthetic Aperture Radars) imagery databases (especially the labeled ones) and sparsity of pre-trained neural networks lead to the need for heavy data generation, augmentation, or transfer learning usage. This paper described the characteristics of SAR imagery, the limitations related to it, and a small set of available databases. Comprehensive data augmentation methods for training Neural Networks were presented, and a novel filter-based method was proposed. The new method limits the effect of the speckle noise, which is very high-level in SAR imagery. The improvement in the dataset could be clearly registered in the loss value functions. The main advantage comes from more developed feature detectors for filter-based training, which is shown in the layer-wise feature analysis. The author attached the trained neural networks for open use. This provides quicker CNN-based solutions implementation.

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“…The neural network would be able to focus on shadows and high objects for such a task. This can be advantageous, as shown in [26].…”

Section: Self-supervised Learning Techniquesmentioning

confidence: 99%

Unsupervised SAR Imagery Feature Learning with Median Filter-Based Loss Value

Gromada

2022

Sensors

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“…The term UAS refers to a platform that includes unmanned aerial vehicles (UAVs), commonly called drones, as well as communication sensors and a ground control station including a human flight crew [33,34]. Using the UAS, a wide area can be filmed in real time, and communication sensors can be used to provide real-time data [24,35,36]. The safety manager of the construction site detects safety risks at the site and takes corrective actions but cannot perform real-time monitoring simultaneously on sites with extensive areas [37,38].…”

Section: Uas For Construction Safety Inspectionmentioning

confidence: 99%

A Computational Model for Simulating the Performance of UAS-Based Construction Safety Inspection through a System Approach

Jeong,

Yu,

Lee

et al. 2023

Drones

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Recent studies have been focusing on unmanned aircraft systems (UASs) to inspect safety issues in the construction industry. A UAS can monitor a broad range in real time and identify unsafe situations and objects at the jobsite. The related studies mostly focus on technological development, and there are few studies investigating potential performance that can be obtained by implementing UASs in the construction domain. Hence, the main objective of this research is to evaluate the potential of UAS-based construction safety inspection. To achieve the goal, this study developed a system dynamic (SD) model, and scenario analysis was conducted. When compared to the existing methods, the use of a UAS resulted in improved safety inspection performance, reduced possibility of incidents, reduced worker fatigue, and reduced amount of delayed work. The results of this research verified that UAS-based safety inspections can be more effective than existing methods. The results of this study can contribute to the understanding of UAS-based construction safety inspection technologies and the potential of the technology.

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“…In addition, many methods have been proposed for realtime object detection in UAV images [18], [19], [20], [21], [22]. Zhang et al [18] achieved real-time object detection implementation for UAVs by introducing channel-level sparsity in the convolutional layer.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Method for Detecting Dense and Small Objects in UAV Images

Li,

Zhou,

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Object detection in UAV images is an important and challenging task for many applications, which often needs highly efficient detection algorithms to meet the accuracy and real-time requirements of the applications. In this paper, we investigate efficient mechanisms for detecting dense and small objects in UAV images. Specifically, 1) kernel K-means is used to obtain optimal anchors for dense and small object detection; 2) a spatial information enhancement module (SIE) is proposed to improve the detection accuracy of dense objects by extracting object spatial location information; 3) a Coord_C3 module is proposed to improve the receptive field of the network and to reduce the number of network parameters; 4) a small detection head is added in the Head of network and skip connections are employed in the Neck of network to improve the detection accuracy of small objects. Experimental results on the VisDrone-2019, LEVIR-ship and Stanford Drone datasets show that our method not only has higher detection accuracy, but also runs faster compared to state-of-the-art detection methods.

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Real-Time Object Detection and Classification by UAV Equipped With SAR

Cited by 15 publications

References 29 publications

Unsupervised SAR Imagery Feature Learning with Median Filter-Based Loss Value

Unsupervised SAR Imagery Feature Learning with Median Filter-Based Loss Value

A Computational Model for Simulating the Performance of UAS-Based Construction Safety Inspection through a System Approach

An Efficient Method for Detecting Dense and Small Objects in UAV Images

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