Measuring tree stem diameters and straightness with depth-image computer vision

Current techniques of forest inventory rely on manual measurements and are slow and labor intensive.Recent developments in computer vision and depth sensing can produce accurate measurement data at significantly reduced time and labor costs.We developed the ForSense system to measure the diameters of trees at various points along the stem as well as stem straightness.Time use,mean absolute error(MAE),and root mean squared error(RMSE)metrics were used to compare the system against manual methods,and to compare the system against itself(reproducibility).Depth-derived diameter measurements of the stems at the heights of 0.3,1.4,and 2.7 m achieved RMSE of 1.7,1.5,and 2.7 cm,respectively.The ForSense system produced straightness measurement data that was highly correlated with straightness ratings by trained foresters.The ForSense system was also consistent,achieving sub-centimeter diameter difference with subsequent measures and less than 4%difference in straightness value between runs.This method of forest inventory,which is based on depth-image computer vision,is time efficient compared to manual methods and less computationally and technologically intensive compared to Structure-from-Motion(SFM)photogrammetry and ground-based LiDAR or terrestrial laser scanning(TLS).

Fusion of color and hallucinated depth features for enhanced multimodal deep learning-based damage segmentation

Recent advances in computer vision and deep learning have shown that the fusion of depth information can significantly enhance the performance of RGB-based damage detection and segmentation models.However,alongside the advantages,depth-sensing also presents many practical challenges.For instance,the depth sensors impose an additional payload burden on the robotic inspection platforms limiting the operation time and increasing the inspection cost.Additionally,some lidar-based depth sensors have poor outdoor performance due to sunlight contamination during the daytime.In this context,this study investigates the feasibility of abolishing depth-sensing at test time without compromising the segmentation performance.An autonomous damage segmentation framework is developed,based on recent advancements in vision-based multi-modal sensing such as modality hallucination(MH)and monocular depth estimation(MDE),which require depth data only during the model training.At the time of deployment,depth data becomes expendable as it can be simulated from the corresponding RGB frames.This makes it possible to reap the benefits of depth fusion without any depth perception per se.This study explored two different depth encoding techniques and three different fusion strategies in addition to a baseline RGB-based model.The proposed approach is validated on computer-generated RGB-D data of reinforced concrete buildings subjected to seismic damage.It was observed that the surrogate techniques can increase the segmentation IoU by up to 20.1%with a negligible increase in the computation cost.Overall,this study is believed to make a positive contribution to enhancing the resilience of critical civil infrastructure.

A spatial resolution effect analysis of remote sensing bathymetry

A spatial resolution effect of remote sensing bathymetry is an important scientific problem. The in situ measured water depth data and images of Dongdao Island are used to study the effect of water depth inversion from different spatial resolution remote sensing images. The research experiments are divided into five groups including Quick Bird and World View-2 remote sensing images with their original spatial resolution（2.4/2.0 m）and four kinds of reducing spatial resolution（4, 8, 16 and 32 m）, and the water depth control and checking points are set up to carry out remote sensing water depth inversion. The experiment results indicate that the accuracy of the water depth remote sensing inversion increases first as the spatial resolution decreases from 2.4/2.0 to 4, 8 and16 m. And then the accuracy decreases along with the decreasing spatial resolution. When the spatial resolution of the image is 16 m, the inversion error is minimum. In this case, when the spatial resolution of the remote sensing image is 16 m, the mean relative errors（MRE） of Quick Bird and World View-2 bathymetry are 21.2% and 13.1%,compared with the maximum error are decreased by 14.7% and 2.9% respectively; the mean absolute errors（MAE） are 2.0 and 1.4 m, compared with the maximum are decreased by 1.0 and 0.5 m respectively. The results provide an important reference for the selection of remote sensing data in the study and application of the remote sensing bathymetry.

Nanoindentation Measurements of Mechanical Properties of Polyurethane Elastomers Which Crosslinked by β-Cyclodextrin

A series of PUEs which use β-CD as cross-linker were synthesized. Nanoindentation measurements of mechanical properties of these PUEs were made. Load and depth sensing indentation and nano DMA mode were used to evaluate mechanical properties of PUEs in nano-scale. The difference between the results from two modes proved the microphase separation in PUEs and to investigate PUE from hard domains and soft domains was of great significance.