A table method for coded target decoding with application to 3-D reconstruction of soil specimens during triaxial testing

Xia, Xiaolong; Zhang, Xiong; Fayek, Sara; Yin, Zhaozheng

doi:10.1007/s11440-021-01343-1

Cited by 11 publications

(4 citation statements)

References 16 publications

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“…For example, Mohammadi et al [10,11] adopted technology combining terrestrial laser scanning and UAV photogrammetry for the application of bridge monitoring. Coded targets are distinguishable targets, so they can be automatically identified and quickly matched due to the unique identities, which can be used not only in close-range photogrammetry but also in machine vision, robot navigation, augmented reality, three-dimensional (3D) scanning surface reconstruction, and many other fields [12][13][14]. In close-range or UAV photogrammetry and 3D reconstruction, coded targets are always used as tie points between images with overlapping regions, which is crucial for bundle adjustment [15].…”

Section: Introductionmentioning

confidence: 99%

A Robust and Effective Identification Method for Point-Distributed Coded Targets in Digital Close-Range Photogrammetry

et al. 2022

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In close-range or unmanned aerial vehicle (UAV) photogrammetry, Schneider concentric circular coded targets (SCTs), which are public, are widely used for image matching and as ground control points. GSI point-distributed coded targets (GCTs), which are only mainly applied in a video-simultaneous triangulation and resection system (V-STARS), are non-public and rarely applied in UAV photogrammetry. In this paper, we present our innovative detailed solution to identify GCTs. First, we analyze the structure of a GCT. Then, a special 2D P2-invariant of five coplanar points derived from cross ratios is adopted in template point registration and identification. Finally, the affine transformation is used for decoding. Experiments indoors—including different viewing angles ranging from 0° to 80° based on 6 mm-diameter GCTs, smaller 3 mm-diameter GCTs, and different sizes mixed—and outdoors with challenging scenes were carried out. Compared with V-STARS, the results show that the proposed method can preserve the robustness and achieves a high accuracy rate in identification when the viewing angle is not larger than 65° through indoor experiments, and the proposed method can achieve approximate or slightly weaker effectiveness than V-STARS on the whole. Finally, we attempted to extend and apply the designed GCTs in UAV photogrammetry for a preliminary experiment. This paper demonstrates that GCTs can be designed, printed, and identified easily through our method. It is expected that the proposed method may be helpful when applied to image matching, camera calibration, camera orientation, or 3D measurements or serving as control points in UAV photogrammetry for scenarios with complex structures in the future.

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Section: Introductionmentioning

confidence: 99%

A Robust and Effective Identification Method for Point-Distributed Coded Targets in Digital Close-Range Photogrammetry

et al. 2022

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“…In addition, coded targets can also be used as ordered control points for automatic spatial resection to obtain the camera's exterior orientation elements [2]. It can be used not only for close-range photogrammetry, but also for machine vision, robot navigation, augmented reality, 3D scanning surface reconstruction, and many other areas [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

CT-YoloTrad: fast and accurate recognition of point-distributed coded targets for UAV images incorporating CT-YOLOv7

Li,

Wang,

Wang

et al. 2024

Phys. Scr.

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Artificial point-distributed coded targets owns unique coded sequence numbers that can be recognized automatically. To address the issue of decreasing recognition accuracy and efficiency of existing recognition methods in complicated circumstances, an improved object detection model for coded target acquisition from unmanned aerial vehicle (UAV) images, CT-YOLOv7, is proposed. This improved model is based on the original YOLOv7 model, replacing several Conv with partial convolution (PConv), while introducing the bi-level routing attention mechanism, and designing the CBS-R structure and CBS-PR structure. In addition, the loss function is replaced with WIOU loss function to further improve the model’s performance. Based on the above, the new recognition method of point-distributed coded targets for UAV images is organized as follows. Firstly, CT-YOLOv7 is embedded into the front-end of the classical coded targets recognition process (that is, the coded targets are first extracted). Then, the extraction results are fed into the classical recognition algorithm for recognition. Lastly, the recognition results are inverse-calculated back to the origi-nal image. The new method aims to focus the processing on the region of interest to achieve fast and accurate coded targets recognition for UAV images. The experi-mental results show that CT-YOLOv7’s detection accuracy is 90.83%, which im-proves the accuracy by 8.46% and reduces the computation by 11.54% compared to the original YOLOv7. By incorporating the CT-YOLOv7 model, the time consump-tion for coded target recognition of a single UAV image is 150-350ms, which im-proves the average efficiency by 3-5 times compared with the classical method. Fur-thermore, the proposed method can correctly recognize regions with shadows and noise, and the recognition accuracy is improved by 15%-40%. With the method pro-posed in this paper, the coded targets are expected to be applied into UAV photo-grammetry or remote sensing to realize accurate and quasi-real-time recognition.

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“…For this reason, coded targets can be specially applied in fast image matching, object tracking, and automatic high-precision camera orientation [3][4][5]. According to their application areas, coded targets can be used in robot navigation, augmented reality, close-range/UAV photogrammetry, and many other fields [6,7]. For example, Chan et al adopted coded targets to realize self-localization of an uncalibrated camera [5].…”

Section: Introductionmentioning

confidence: 99%

Improved Identification for Point-Distributed Coded Targets with Self-Adaption and High Accuracy in Photogrammetry

et al. 2023

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A robust and effective method for the identification of point-distributed coded targets (IPCT) in a video-simultaneous triangulation and resection system (V-STARS) was reported recently. However, its limitations were the setting of critical parameters, it being non-adaptive, making misidentifications in certain conditions, having low positioning precision, and its identification effect being slightly inferior to that of the V-STARS. Aiming to address these shortcomings of IPCT, an improved IPCT, named I-IPCT, with an adaptive binarization, a more precise ellipse-center localization, and especially an invariance of the point–line distance ratio (PLDR), was proposed. In the process of edge extraction, the adaptive threshold Gaussian function was adopted to realize the acquisition of an adaptive binarization threshold. For the process of center positioning of round targets, the gray cubic weighted centroid algorithm was adopted to realize high-precision center localization. In the template point recognition procedure, the invariant of the PLDR was used to realize the determination of template points adaptively. In the decoding procedure, the invariant of the PLDR was adopted to eliminate confusion. Experiments in indoor, outdoor, and unmanned aerial vehicle (UAV) settings were carried out; meanwhile, sufficient comparisons with IPCT and V-STARS were performed. The results show that the improvements can make the identification approximately parameter-free and more accurate. Meanwhile, it presented a high three-dimensional measurement precision in close-range photogrammetry. The improved IPCT performed equally well as the commercial software V-STARS on the whole and was slightly superior to it in the UAV test, in which it provided a fantastic open solution using these kinds of coded targets and making it convenient for researchers to freely apply the coded targets in many aspects, including UAV photogrammetry for high-precision automatic image matching and three-dimensional real-scene reconstruction.

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A table method for coded target decoding with application to 3-D reconstruction of soil specimens during triaxial testing

Cited by 11 publications

References 16 publications

A Robust and Effective Identification Method for Point-Distributed Coded Targets in Digital Close-Range Photogrammetry

A Robust and Effective Identification Method for Point-Distributed Coded Targets in Digital Close-Range Photogrammetry

CT-YoloTrad: fast and accurate recognition of point-distributed coded targets for UAV images incorporating CT-YOLOv7

Improved Identification for Point-Distributed Coded Targets with Self-Adaption and High Accuracy in Photogrammetry

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