Extrinsic Parameter Calibration for Line Scanning Cameras on Ground Vehicles with Navigation Systems Using a Calibration Pattern

Wendel, Alexander; Underwood, James

doi:10.3390/s17112491

Cited by 10 publications

(8 citation statements)

References 39 publications

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“…However, the workflow of imaging spectroscopy presents some challenges (Aasen et al ., ), such as the radiometric correction required to turn images into reflectance or the geometric corrections required to obtain georeferenced images. However, recent advances in ground‐based imaging spectroscopy (Underwood et al ., ; Wendel & Underwood, ,b) present an opportunity for implementing novel algorithms for phenotyping photosynthesis‐based traits using hyperspectral sensing at the canopy level.…”

Section: Biomass Radiation Use Efficiency and Photosynthesis: New Fmentioning

confidence: 99%

Field crop phenomics: enabling breeding for radiation use efficiency and biomass in cereal crops

et al. 2019

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Summary Plant phenotyping forms the core of crop breeding, allowing breeders to build on physiological traits and mechanistic science to inform their selection of material for crossing and genetic gain. Recent rapid progress in high‐throughput techniques based on machine vision, robotics, and computing (plant phenomics) enables crop physiologists and breeders to quantitatively measure complex and previously intractable traits. By combining these techniques with affordable genomic sequencing and genotyping, machine learning, and genome selection approaches, breeders have an opportunity to make rapid genetic progress. This review focuses on how field‐based plant phenomics can enable next‐generation physiological breeding in cereal crops for traits related to radiation use efficiency, photosynthesis, and crop biomass. These traits have previously been regarded as difficult and laborious to measure but have recently become a focus as cereal breeders find genetic progress from ‘Green Revolution’ traits such as harvest index become exhausted. Application of LiDAR, thermal imaging, leaf and canopy spectral reflectance, Chl fluorescence, and machine learning are discussed using wheat and sorghum phenotyping as case studies. A vision of how crop genomics and high‐throughput phenotyping could enable the next generation of crop research and breeding is presented.

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Section: Biomass Radiation Use Efficiency and Photosynthesis: New Fmentioning

confidence: 99%

Field crop phenomics: enabling breeding for radiation use efficiency and biomass in cereal crops

et al. 2019

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“…As shown in Figure 12 , a hyperspectral line-scan camera was mounted on an agricultural robot, Ladybird. Ladybird was designed and built at the Australian Centre for Field Robotics (ACFR) at The University of Sydney as a flexible tool to support a range of agricultural research applications [ 28 , 29 , 30 , 31 , 32 ]. The Resonon Pika XC-2 visible to near infrared (VNIR) hyperspectral line-scan camera is mounted on top of the Ladybird platform and oriented such that the scan line is horizontal and pitched down for scanning the ground surface.…”

Section: Validationmentioning

confidence: 99%

Improved Cross-Ratio Invariant-Based Intrinsic Calibration of A Hyperspectral Line-Scan Camera

Bender

Sukkarieh

2018

Sensors

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Hyperspectral line-scan cameras are increasingly being deployed on mobile platforms operating in unstructured environments. To generate geometrically accurate hyperspectral composites, the intrinsic parameters of these cameras must be resolved. This article describes a method for determining the intrinsic parameters of a hyperspectral line-scan camera. The proposed method is based on a cross-ratio invariant calibration routine and is able to estimate the focal length, principal point, and radial distortion parameters in a hyperspectral line-scan camera. Compared to previous methods that use similar calibration targets, our approach extends the camera model to include radial distortion. It is able to utilize calibration data recorded from multiple camera view angles by optimizing the re-projection error of all calibration data jointly. The proposed method also includes an additional signal processing step that automatically detects calibration points in hyperspectral imagery of the calibration target. These contributions result in accurate estimates of the intrinsic parameters with minimal supervision. The proposed method is validated through comprehensive simulation and demonstrated on real hyperspectral line-scans.

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“…We used the method of line scan imaging, which we think is most effective in technology that can automatically acquire images while driving at high speeds. [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Detection and Identification of Expansion Joint Gap of Road Bridges by Machine Learning Using Line-Scan Camera Images

Kim

Cho

et al. 2021

ASI

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Recently, the lack of expansion joint gaps on highway bridges in Korea has been increasing. In particular, with the increase in the number of days during the summer heatwave, the narrowing of the expansion joint gap causes symptoms such as expansion joint damage and pavement blow-up, which threaten traffic safety and structural safety. Therefore, in this study, we developed a machine vision (M/V)-technique-based inspection system that can monitor the expansion joint gap through image analysis while driving at high speed (100 km/h), replacing the current manual method that uses an inspector to inspect the expansion joint gap. To fix the error factors of image analysis that happened during the trial application, a machine learning method was used to improve the accuracy of measuring the gap between the expansion joint device. As a result, the expansion gap identification accuracy was improved by 27.5%, from 67.5% to 95.0%, and the use of the system reduces the survey time by more than 95%, from an average of approximately 1 h/bridge (existing manual inspection method) to approximately 3 min/bridge. We assume, in the future, maintenance practitioners can contribute to preventive maintenance that prepares countermeasures before problems occur.

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Extrinsic Parameter Calibration for Line Scanning Cameras on Ground Vehicles with Navigation Systems Using a Calibration Pattern

Cited by 10 publications

References 39 publications

Field crop phenomics: enabling breeding for radiation use efficiency and biomass in cereal crops

Field crop phenomics: enabling breeding for radiation use efficiency and biomass in cereal crops

Improved Cross-Ratio Invariant-Based Intrinsic Calibration of A Hyperspectral Line-Scan Camera

Detection and Identification of Expansion Joint Gap of Road Bridges by Machine Learning Using Line-Scan Camera Images

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