scholarly journals Field-Deployable Computer Vision Wood Identification of Peruvian Timbers

2021 ◽  
Vol 12 ◽  
Author(s):  
Prabu Ravindran ◽  
Frank C. Owens ◽  
Adam C. Wade ◽  
Patricia Vega ◽  
Rolando Montenegro ◽  
...  
Keyword(s):  
Computer Vision ◽  
Training Data ◽  
Identification System ◽  
Wood Identification ◽  
Illegal Logging ◽  
Natural Protected Areas ◽  
Indigenous Territories ◽  
Timber Trade ◽  
The Tropics

Illegal logging is a major threat to forests in Peru, in the Amazon more broadly, and in the tropics globally. In Peru alone, more than two thirds of logging concessions showed unauthorized tree harvesting in natural protected areas and indigenous territories, and in 2016 more than half of exported lumber was of illegal origin. To help combat illegal logging and support legal timber trade in Peru we trained a convolutional neural network using transfer learning on images obtained from specimens in six xylaria using the open source, field-deployable XyloTron platform, for the classification of 228 Peruvian species into 24 anatomically informed and contextually relevant classes. The trained models achieved accuracies of 97% for five-fold cross validation, and 86.5 and 92.4% for top-1 and top-2 classification, respectively, on unique independent specimens from a xylarium that did not contribute training data. These results are the first multi-site, multi-user, multi-system-instantiation study for a national scale, computer vision wood identification system evaluated on independent scientific wood specimens. We demonstrate system readiness for evaluation in real-world field screening scenarios using this accurate, affordable, and scalable technology for monitoring, incentivizing, and monetizing legal and sustainable wood value chains.

scholarly journals Imaged based identification of colombian timbers using the xylotron: a proof of concept international partnership

2021 ◽  
Vol 24 (1) ◽  
pp. 5-16
Author(s):  
Rafael E. Arévalo B. ◽  
Esperanza N. Pulido R. ◽  
Juan F. Solórzano G. ◽  
Richard Soares ◽  
Flavio Ruffinatto ◽  
...  
Keyword(s):  
Computer Vision ◽  
Image Data ◽  
Field Testing ◽  
Wood Identification ◽  
Proof Of Concept ◽  
Illegal Logging ◽  
Data Set ◽  
Final Model ◽  
International Partnership ◽  
Initial Identification

Field deployable computer vision wood identification systems can play a key role in combating illegal logging in the real world. This work used 764 xylarium specimens from 84 taxa to develop an image data set to train a classifier to identify 14 commercial Colombian timbers. We imaged specimens from various xylaria outside Colombia, trained and evaluated an initial identification model, then collected additional images from a Colombian xylarium (BOFw), and incorporated those images to refine and produce a final model. The specimen classification accuracy of this final model was ~ 97%, demonstrating that including local specimens can augment the accuracy and reliability of the XyloTron system. Our study demonstrates the first deployable computer vision model for wood identification in Colombia, developed on a timescale of months rather than years by leveraging international cooperation. We conclude that field testing and advanced forensic and machine learning training are the next logical steps.

scholarly journals The XyloPhone: democratizing access to high-quality macroscopic imaging for wood and other substrates

2020 ◽  
Author(s):  
Alex C. Wiedenhoeft
Keyword(s):  
Computer Vision ◽  
Spherical Aberration ◽  
Research Quality ◽  
Limiting Factor ◽  
Identification System ◽  
Wood Identification ◽  
Focal Distance ◽  
Uv Illumination ◽  
3D Printed ◽  
Rate Limiting

AbstractOne rate-limiting factor in the fight against illegal logging is the lack of powerful, affordable, scalable wood identification tools for field screening. Computer vision wood identification using smartphones fitted with customized imaging peripherals offer a potential solution but to date, such peripherals suffer from one or more weaknesses: low image quality, lack of lighting control, uncontrolled magnification, unknown distortion and spherical aberration, and/or no access to or publication of the system design. To address cost, optical concerns, and open access to designs and parameters, I present the XyloPhone, a 3D printed research quality macroscopic imaging attachment adaptable to any smartphone. It provides a fixed focal distance, exclusion of ambient light, selection of visible light or UV illumination, uses the lens from a commercially available loupe, is powered by a rechargeable external battery, is fully open-sourced, and at a price point of less than 110 USD is a highly affordable tool for the laboratory or the field, and can serve as the foundational hardware for a scalable field deployable computer vision wood identification system.

scholarly journals The XyloPhone: toward democratizing access to high-quality macroscopic imaging for wood and other substrates

IAWA Journal ◽  
2020 ◽  
Vol 41 (4) ◽  
pp. 699-719
Author(s):  
Alex C. Wiedenhoeft
Keyword(s):  
Computer Vision ◽  
Spherical Aberration ◽  
Research Quality ◽  
Limiting Factor ◽  
Identification System ◽  
Wood Identification ◽  
Focal Distance ◽  
Uv Illumination ◽  
3D Printed ◽  
Rate Limiting

Abstract One rate-limiting factor in the fight against illegal logging is the lack of powerful, affordable, scalable wood identification tools for field screening. Computer vision wood identification using smartphones fitted with customized imaging peripherals offers a potential solution, but to date, such peripherals suffer from one or more weaknesses: low image quality, lack of lighting control, uncontrolled magnification, unknown distortion, and spherical aberration, and/or no access to or publication of the system design. To address cost, optical concerns, and open access to designs and parameters, I present the XyloPhone, a 3D printed research quality macroscopic imaging attachment adaptable to virtually any smartphone. It provides a fixed focal distance, exclusion of ambient light, selection of visible light or UV illumination, uses the lens from a commercially available loupe, is powered by a rechargeable external battery, is fully open-sourced, at a price point of less than USD 110 is a highly affordable tool for the laboratory or the field, and can serve as the foundational hardware for a scalable field-deployable computer vision wood identification system.

scholarly journals NLOS Multipath Classification of GNSS Signal Correlation Output Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2503
Author(s):  
Taro Suzuki ◽  
Yoshiharu Amano
Keyword(s):  
Machine Learning ◽  
Satellite System ◽  
Training Data ◽  
Support Vector ◽  
Positioning Errors ◽  
Automated Method ◽  
Global Navigation Satellite ◽  
Better Than ◽  
Signal Correlation

This paper proposes a method for detecting non-line-of-sight (NLOS) multipath, which causes large positioning errors in a global navigation satellite system (GNSS). We use GNSS signal correlation output, which is the most primitive GNSS signal processing output, to detect NLOS multipath based on machine learning. The shape of the multi-correlator outputs is distorted due to the NLOS multipath. The features of the shape of the multi-correlator are used to discriminate the NLOS multipath. We implement two supervised learning methods, a support vector machine (SVM) and a neural network (NN), and compare their performance. In addition, we also propose an automated method of collecting training data for LOS and NLOS signals of machine learning. The evaluation of the proposed NLOS detection method in an urban environment confirmed that NN was better than SVM, and 97.7% of NLOS signals were correctly discriminated.

scholarly journals Geometric and Topological Bases of a New Classification of Wood Vascular Tissues Part 1: Shape and Arrangement Classifications of Vessels

2021 ◽  
Vol 13 (14) ◽  
pp. 7545
Author(s):  
Nikolai Bardarov ◽  
Vladislav Todorov ◽  
Nicole Christoff
Keyword(s):  
Computer Vision ◽  
Detailed Analysis ◽  
Tree Species ◽  
Complete Solution ◽  
New Classification ◽  
Topological Characteristics ◽  
Definition Of ◽  
Wood Science ◽  
Quantitative Indicators

The need to identify wood by its anatomical features requires a detailed analysis of all the elements that make it up. This is a significant problem of structural wood science, the most general and complete solution of which is yet to be sought. In recent years, increasing attention has been paid to the use of computer vision methods to automate processes such as the detection, identification, and classification of different tissues and different tree species. The more successful use of these methods in wood anatomy requires a more precise and comprehensive definition of the anatomical elements, according to their geometric and topological characteristics. In this article, we conduct a detailed analysis of the limits of variation of the location and grouping of vessels in the observed microscopic samples. The present development offers criteria and quantitative indicators for defining the terms shape, location, and group of wood tissues. It is proposed to differentiate the quantitative indicators of the vessels depending on their geometric and topological characteristics. Thus, with the help of computer vision technics, it will be possible to establish topological characteristics of wood vessels, the extraction of which would be used to develop an algorithm for the automatic classification of tree species.

scholarly journals Artificial Intelligence, 3D Documentation, and Rock Art—Approaching and Reflecting on the Automation of Identification and Classification of Rock Art Images

2021 ◽  
Author(s):  
Christian Horn ◽  
Oscar Ivarsson ◽  
Cecilia Lindhé ◽  
Rich Potter ◽  
Ashely Green ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Bronze Age ◽  
Data Augmentation ◽  
Rock Art ◽  
Training Data ◽  
Future Research ◽  
Rock Surface ◽  
Southern Scandinavia ◽  
Bounding Boxes

AbstractRock art carvings, which are best described as petroglyphs, were produced by removing parts of the rock surface to create a negative relief. This tradition was particularly strong during the Nordic Bronze Age (1700–550 BC) in southern Scandinavia with over 20,000 boats and thousands of humans, animals, wagons, etc. This vivid and highly engaging material provides quantitative data of high potential to understand Bronze Age social structures and ideologies. The ability to provide the technically best possible documentation and to automate identification and classification of images would help to take full advantage of the research potential of petroglyphs in southern Scandinavia and elsewhere. We, therefore, attempted to train a model that locates and classifies image objects using faster region-based convolutional neural network (Faster-RCNN) based on data produced by a novel method to improve visualizing the content of 3D documentations. A newly created layer of 3D rock art documentation provides the best data currently available and has reduced inscribed bias compared to older methods. Several models were trained based on input images annotated with bounding boxes produced with different parameters to find the best solution. The data included 4305 individual images in 408 scans of rock art sites. To enhance the models and enrich the training data, we used data augmentation and transfer learning. The successful models perform exceptionally well on boats and circles, as well as with human figures and wheels. This work was an interdisciplinary undertaking which led to important reflections about archaeology, digital humanities, and artificial intelligence. The reflections and the success represented by the trained models open novel avenues for future research on rock art.

scholarly journals Marine Network Protocols and Security Risks

2021 ◽  
Vol 1 (2) ◽  
pp. 239-251
Author(s):  
Ky Tran ◽  
Sid Keene ◽  
Erik Fretheim ◽  
Michail Tsikerdekis
Keyword(s):  
Network Protocols ◽  
Network Protocol ◽  
Identification System ◽  
Automated Identification ◽  
Security Risks ◽  
Domain Specific ◽  
Protocol Security ◽  
Challenges And Opportunities

Marine network protocols are domain-specific network protocols that aim to incorporate particular features within the specialized marine context that devices are implemented in. Devices implemented in such vessels involve critical equipment; however, limited research exists for marine network protocol security. In this paper, we provide an analysis of several marine network protocols used in today’s vessels and provide a classification of attack risks. Several protocols involve known security limitations, such as Automated Identification System (AIS) and National Marine Electronic Association (NMEA) 0183, while newer protocols, such as OneNet provide more security hardiness. We further identify several challenges and opportunities for future implementations of such protocols.

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