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Vision-based fatigue crack detection using global motion compensation and video feature tracking

Earthquake Engineering and Engineering Vibration 2023年第1期22卷 19-39页

作者： Rushil Mojidra Jian Li Ali Mohammadkhorasani Fernando Moreu caroline bennett William Collins Department of Civil Environmental and Architectural EngineeringThe University of KansasLawrenceKansasUSA Department of Civil Construction and Environmental EngineeringUniversity of New MexicoAlbuquerqueNew MexicoUSA

Fatigue cracks that develop in civil infrastructure such as steel bridges due to repetitive loads pose a major threat to structural *** being the most common practice for fatigue crack detection,human visual inspection is known to be labor intensive,time-consuming,and prone to *** this study,a computer vision-based fatigue crack detection approach using a short video recorded under live loads by a moving consumer-grade camera is *** method detects fatigue crack by tracking surface motion and identifies the differential motion pattern caused by opening and closing of the fatigue ***,the global motion introduced by a moving camera in the recorded video is typically far greater than the actual motion associated with fatigue crack opening/closing,leading to false detection *** overcome the challenge,global motion compensation(GMC)techniques are introduced to compensate for camera-induced *** particular,hierarchical model-based motion estimation is adopted for 2D videos with simple geometry and a new method is developed by extending the bundled camera paths approach for 3D videos with complex *** proposed methodology is validated using two laboratory test setups for both in-plane and out-of-plane fatigue *** results confirm the importance of motion compensation for both 2D and 3D videos and demonstrate the effectiveness of the proposed GMC methods as well as the subsequent crack detection algorithm.

关键词： global motion compensation fatigue crack detection computer vision parallax effect distortion induced fatigue crack video stabilization camera motion in-plane fatigue crack out-of-plane fatigue crackanalysis

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冲刷作用下砂土地层中桩的侧向力学性质分析

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南方能源建设 2018年第2期5卷 24-36页

作者：林城韩杰 caroline bennett Robert PARSONS 加拿大维多利亚大学土木工程系美国堪萨斯大学土木环境与建筑系

[目的]冲刷是在水流以及波浪作用下土体流失的一个过程,它通常发生在桥梁、港口码头、以及海岸平台等结构的基础周围,造成基础的承载力下降,危及上部结构的安全。目前在分析桩的侧向力学行为时,只考虑了冲刷深度对桩的侧向承载力的影响... 详细信息

[目的]冲刷是在水流以及波浪作用下土体流失的一个过程,它通常发生在桥梁、港口码头、以及海岸平台等结构的基础周围,造成基础的承载力下降,危及上部结构的安全。目前在分析桩的侧向力学行为时,只考虑了冲刷深度对桩的侧向承载力的影响,而忽略了冲刷形成的冲刷孔的影响。事实上,冲刷孔的几何形状不仅包括冲刷深度、还包括了冲刷孔的宽度以及冲刷孔的坡度。目前,工程设计中广泛采用的侧向桩分析方法,即p-y曲线方法无法考虑冲刷孔三维几何形状对桩的侧向力学性质的影响。[方法]针对这个问题,文章提出了一种修正的p-y曲线方法以用于分析砂土地层中桩在冲刷作用下的侧向力学响应。[结果]该方法改进了基于锲形体破坏模型的Reese p-y曲线法,从而能考虑冲刷孔三维几何形状的影响。将计算得到的结果与三维有限差分数值分析的结果进行了比较,验证该修正方法的可靠性。[结论]计算结果表明:在冲刷孔的几何形状参数里,冲刷深度对桩的侧向力学性质的影响最大,其次是冲刷孔的宽度与坡度。忽略冲刷孔几何形状的影响,桩在地面位置的侧向位移会增加49%~68%。

关键词：侧向位移侧向力数值方法桩

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Use of Unmanned Aerial System (UAS) Phenotyping to Predict Pod and Seed Yield in Organic Peanuts

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American Journal of Plant Sciences 2023年第3期14卷 415-426页

作者： Aurora Manley Waltram Ravelombola John Cason Brian bennett Hanh Pham Emi Kimura caroline Ruhl Waqas Ahmad Madeline Brown Texas A&M AgriLife Research Vernon Texas USA Department of Soil and Crop Sciences Texas A&M University College Station Texas USA Texas A&M AgriLife Research Stephenville Texas USA Texas A&M AgriLife Research Lubbock Texas USA Texas A&M AgriLife Extension Vernon Texas USA

Peanut (Arachis hypogaea L.) is a highly nutritious food that is an excellent source of protein and is associated with increased coronary health, lower risk of type-2 diabetes, lower risk of breast cancer and a healthy profile of inflammatory biomarkers. The domestic demand for organic peanuts has significantly increased, requiring new breeding efforts to develop peanut varieties adapted to the organic farming system. The use of unmanned aerial system (UAS) has gained scientific attention because of the ability to generate high-throughput phenotypic data. However, it has not been fully investigated for phenotyping agronomic traits of organic peanuts. Peanuts are beneficial for cardio system protection and are widely used. Within the U.S., peanuts are grown in 11 states on roughly 600,000 hectares and averaging 4500 kg/ha. This study’s objective was to test the accuracy of UAS data in the phenotyping pod and seed yield of organic peanuts. UAS data was collected from a field plot with 20 Spanish peanut breeding lines on July 07, 2021 and September 27, 2021. The study was a randomized complete block design (RCBD) with 3 blocks. Twenty-five vegetation indices (VIs) were calculated. The analysis of variance showed significant genotypic effects on all 25 vegetation indices for both flights (p < 0.05). The vegetation index Red edge (RE) from the first flight was the most significantly correlated with both pod (r = 0.44) and seed yield (r = 0.64). These results can be used to further advance organic peanut breeding efforts with high-throughput data collection.

关键词： Peanut Unmanned Aerial System Vegetation Indices Phenotyping Pod Yield Seed Yield

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