Portable Imagery Quality Assessment Test Field for Uav Sensors

Dąbrowski, Rafał; Jenerowicz, Agnieszka

doi:10.5194/isprsarchives-xl-1-w4-117-2015

Cited by 9 publications

(3 citation statements)

References 23 publications

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“…Human intelligence (HUMINT) [9]; • Signals intelligence (SIGINT) [10]; • Imagery intelligence (IMINT) [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Military Decision-Making Process Enhanced by Image Detection

Žigulić,

Glučina,

Lorencin

et al. 2023

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This study delves into the vital missions of the armed forces, encompassing the defense of territorial integrity, sovereignty, and support for civil institutions. Commanders grapple with crucial decisions, where accountability underscores the imperative for reliable field intelligence. Harnessing artificial intelligence, specifically, the YOLO version five detection algorithm, ensures a paradigm of efficiency and precision. The presentation of trained models, accompanied by pertinent hyperparameters and dataset specifics derived from public military insignia videos and photos, reveals a nuanced evaluation. Results scrutinized through precision, recall, map@0.5, mAP@0.95, and F1 score metrics, illuminate the supremacy of the model employing Stochastic Gradient Descent at 640 × 640 resolution: 0.966, 0.957, 0.979, 0.830, and 0.961. Conversely, the suboptimal performance of the model using the Adam optimizer registers metrics of 0.818, 0.762, 0.785, 0.430, and 0.789. These outcomes underscore the model’s potential for military object detection across diverse terrains, with future prospects considering the implementation on unmanned arial vehicles to amplify and deploy the model effectively.

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“…Human intelligence (HUMINT) [9]; • Signals intelligence (SIGINT) [10]; • Imagery intelligence (IMINT) [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Military Decision-Making Process Enhanced by Image Detection

Žigulić,

Glučina,

Lorencin

et al. 2023

Information

View full text Add to dashboard Cite

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“…In technical specifications, the IFOV parameter is denoted in angular measurements (milliradians). The measurement of a specific detector is referred to as the theoretical ratio of the distance to the size of the measurement spot [ 54 , 55 ].

…”

Section: Introductionmentioning

confidence: 99%

The Conception of Test Fields for Fast Geometric Calibration of the FLIR VUE PRO Thermal Camera for Low-Cost UAV Applications

Fryskowska-Skibniewska

Deliś

Kędzierski

et al. 2022

Sensors

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The dynamic evolution of photogrammetry led to the development of numerous methods of geometric calibration of cameras, which are mostly based on building flat targets (fields) with features that can be distinguished in the images. Geometric calibration of thermal cameras for UAVs is an active research field that attracts numerous researchers. As a result of their low price and general availability, non-metric cameras are being increasingly used for measurement purposes. Apart from resolution, non-metric sensors do not have any other known parameters. The commonly applied process is self-calibration, which enables the determining of the approximate elements of the camera’s interior orientation. The purpose of this work was to analyze the possibilities of geometric calibration of thermal UAV cameras using proposed test field patterns and materials. The experiment was conducted on a FLIR VUE PRO thermal camera dedicated to UAV platforms. The authors propose the selection of various image processing methods (histogram equalization, thresholding, brightness correction) in order to improve the quality of the thermograms. The consecutive processing methods resulted in over 80% effectiveness on average by 94%, 81%, and 80 %, respectively. This effectiveness, for no processing and processing with the use of the filtering method, was: 42% and 38%, respectively. Only high-pass filtering did not improve the obtained results. The final results of the proposed method and structure of test fields were verified on chosen geometric calibration algorithms. The results of fast and low-cost calibration are satisfactory, especially in terms of the automation of this process. For geometric correction, the standard deviations for the results of specific methods of thermogram sharpness enhancement are two to three times better than results without any correction.

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“…Numerous image quality studies have focused on testing digital cameras in general (e.g., [59,60]), or on more specific types of devices and systems such as camera phones (e.g., [61,62]), 360-degree cameras (e.g., [63,64]), and digital aerial imaging sensors (e.g., [65][66][67]). However, the authors have been unable to identify any studies concerning the imaging quality of TLS systems with integrated cameras.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Quality of TLS Point Cloud Colorization

et al. 2020

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Terrestrial laser scanning (TLS) enables the efficient production of high-density colored 3D point clouds of real-world environments. An increasing number of applications from visual and automated interpretation to photorealistic 3D visualizations and experiences rely on accurate and reliable color information. However, insufficient attention has been put into evaluating the colorization quality of the 3D point clouds produced applying TLS. We have developed a method for the evaluation of the point cloud colorization quality of TLS systems with integrated imaging sensors. Our method assesses the capability of several tested systems to reproduce colors and details of a scene by measuring objective image quality metrics from 2D images that were rendered from 3D scanned test charts. The results suggest that the detected problems related to color reproduction (i.e., measured differences in color, white balance, and exposure) could be mitigated in data processing while the issues related to detail reproduction (i.e., measured sharpness and noise) are less in the control of a scanner user. Despite being commendable 3D measuring instruments, improving the colorization tools and workflows, and automated image processing pipelines would potentially increase not only the quality and production efficiency but also the applicability of colored 3D point clouds.

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Portable Imagery Quality Assessment Test Field for Uav Sensors

Cited by 9 publications

References 23 publications

Military Decision-Making Process Enhanced by Image Detection

Military Decision-Making Process Enhanced by Image Detection

The Conception of Test Fields for Fast Geometric Calibration of the FLIR VUE PRO Thermal Camera for Low-Cost UAV Applications

Evaluating the Quality of TLS Point Cloud Colorization

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