Vibration Compensation for Fisheye Lenses in UAV Applications

Gurtner, Alex; Walker, Rodney A.; Boles, Wageeh

doi:10.1109/dicta.2007.4426799

Cited by 6 publications

(6 citation statements)

References 9 publications

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“…Fig. 20 shows the performance of the mosaic process while in the turn of [20][21][22][23][24][25][26][27][28][29][30] • . While the images from below the aircraft (using the distorted part of the lens) are reasonable in clarity, there is still some remaining distortion, and also, the registration of these images is worse than the straight and level case, with the latter fact being due to the limitations of the attitude solution in turns.…”

Section: Discussionmentioning

confidence: 99%

“…Through the observation of numerous images, it could be observed that the distortion of the projected fish-eye image changed relative to the sensor array (a component of the lens moves relative to the sensor). To automatically compensate for the vibration, we used a novel approach based on the application of the Hough transform applied to the partially available fish-eye image circle boundary [24]. Fig.…”

Section: ) Compensation Of a Vibrating Lensmentioning

confidence: 99%

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Investigation of Fish-Eye Lenses for Small-UAV Aerial Photography

Gurtner

Greer

Glassock

et al. 2009

IEEE Trans. Geosci. Remote Sensing

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Abstract-Aerial photography obtained by unmanned aerial vehicles (UAVs) is a rising market for their civil application. Small UAVs are believed to close gaps in niche markets, such as acquiring airborne image data for remote sensing purposes. Small UAVs can fly at low altitudes, in dangerous environments, and over long periods of time. However, their small lightweight construction leads to new problems, such as higher agility and more susceptibility to turbulence, which has a big impact on the quality of the data and their suitability for aerial photography. This paper investigates the use of fish-eye lenses to overcome field-of-view (FOV) issues for highly agile UAV platforms susceptible to turbulence. The fish-eye lens has the benefit of a large observation area (large FOV) and does not add additional weight to the aircraft, such as traditional mechanical stabilizing systems. We present the implementation of a fish-eye lens for aerial photography and mapping purposes, with potential use in remote sensing applications. We describe a detailed investigation from the fish-eye lens distortion to the registering of the images. Results of the process are presented using low-quality sensors typically found on small UAVs. The system was flown on a midsize platform (a more stable Cessna aircraft) and also on ARCAA's small (< 10 kg) UAV platform. The effectiveness of the approach is compared for the two sized platforms.

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Section: Discussionmentioning

confidence: 99%

Section: ) Compensation Of a Vibrating Lensmentioning

confidence: 99%

Investigation of Fish-Eye Lenses for Small-UAV Aerial Photography

Gurtner

Greer

Glassock

et al. 2009

IEEE Trans. Geosci. Remote Sensing

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“…Raw image outputs from the handheld and the Zenmuse cameras produced images with dimensions that were too large and would exceed processor memory capacity. To begin, every input image was cropped to dimensions of 2,880 × 2,880 pixels to remove the “fish-eye” effect that often accompanies aerial imagery (Gurtner et al 2007). Next, these images were tiled into smaller dimensions of 1,024 × 1,024 pixels using Python 3.9.7 (Python Software Foundation 2022) and the Pillow module (Clark 2022).…”

Section: Methodsmentioning

confidence: 99%

Use of open-source object detection algorithms to detect Palmer amaranth (Amaranthus palmeri) in soybean

et al. 2022

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Site-specific weed management using open-sourced object detection algorithms could accurately detect weeds in cropping systems. We investigated the use of object detection algorithms to detect Palmer amaranth (Amaranthus palmeri S. Watson) in soybean [Glycine max (L.) Merr.]. The objectives were to 1) develop an annotated image database of A. palmeri and soybean to fine-tune object detection algorithms, 2) compare effectiveness of multiple open-sourced algorithms in detecting A. palmeri, and 3) evaluate the relationship between A. palmeri growth features and A. palmeri detection ability. Soybean field sites were established in Manhattan, KS and Gypsum, KS with natural populations of A. palmeri. A total of 1108 and 392 images were taken aerially and at ground level, respectively, between May 27 and July 27, 2021. After image annotation, a total of 4492 images were selected. Annotated images were used to fine tune open-source Faster regional convolutional (Faster R-CNN) and Single Shot Detector (SSD) algorithms using a Resnet backbone, as well as the You Only Look Once (YOLO) series algorithms. Results demonstrated that YOLO version 5 achieved the highest mean average precision score of 0.77. For both A. palmeri and soybean detections within this algorithm, the highest F1 score was 0.72 when using a confidence threshold of 0.298. A lower confidence threshold of 0.15 increased the likelihood of species detection, but also increased the likelihood of false positive detections. The trained YOLOv5 dataset was used to identify A. palmeri in a dataset paired with measured growth features. Linear regression models predicted that as A. palmeri densities increased and as A. palmeri height increased, precision, recall, and F1 scores of algorithms decreased. We conclude that open-sourced algorithms such as YOLOv5 show great potential in detecting A. palmeri in soybean cropping systems.

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“…Other studies that investigated the effect of sUAS vibration on image quality were performed by the authors of [14,15]. The latter study not only focused on vibration but also investigated other aspects such as lens calibration, orthorectification and mapping.…”

Section: Methodsmentioning

confidence: 99%

“…The sUAS has variations approximately twice of the variations on the Cessna 172. In [14], where the focus of the investigation was on vibration effects and compensation, the vibration was divided into rotation and translational vibrations. The largest translational movement detected was ±5 pixels measured from randomly selected images.…”

Section: Methodsmentioning

confidence: 99%

A Survey of Practical Design Considerations of Optical Imaging Stabilization Systems for Small Unmanned Aerial Systems

Rodin

Andrade

Hovenburg

et al. 2019

Sensors

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Optical imaging systems are one of the most common sensors used for collecting data with small Unmanned Aerial Systems (sUAS). Plenty of research exists which present custom-made optical imaging systems for specific missions. However, the research commonly leaves out the explanation of design parameters and considerations taken during the design of the optical imaging system, especially the image stabilization strategy used, which is a significant issue in sUAS imaging missions. This paper surveys useful methodologies for designing a stabilized optical imaging system by presenting an overview of the important aspects that must be addressed in the designing phase and which tools and techniques are available and should be chosen according to the design requirements.

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Vibration Compensation for Fisheye Lenses in UAV Applications

Abstract: Abstract

Cited by 6 publications

References 9 publications

Investigation of Fish-Eye Lenses for Small-UAV Aerial Photography

Investigation of Fish-Eye Lenses for Small-UAV Aerial Photography

Use of open-source object detection algorithms to detect Palmer amaranth (Amaranthus palmeri) in soybean

A Survey of Practical Design Considerations of Optical Imaging Stabilization Systems for Small Unmanned Aerial Systems

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