opencv/cvat: v1.1.0

Sekachev, Boris; Manovich, Nikita; Zhiltsov, Maxim; Zhavoronkov, Andrey; Калинин, Д. В.; Hoff, Ben; TOsmanov,; Kruchinin, Dmitry; Zankevich, Artyom; DmitriySidnev,; Markelov, Maksim; Johannes,; Chenuet, Mathis; a-andre,; telenachos,; Melnikov, Aleksandr; Kim, Jijoong; Ilouz, Liron; Glazov, Nikita; Priya, .; Tehrani, Rush; Jeong, Seung-Hwa; Skubriev, Vladimir; Yonekura, Sebastian; truong, vugia; zliang,; lizhming,; Truong, T. K.

doi:10.5281/zenodo.4009388

Cited by 59 publications

(35 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the period starting from the 12th of July until the 8th of August 2021, 195 images were uploaded to the cloud storage service. Of these 195 images a random sample of 100 images were selected in which the crickets were marked with bounding boxes using the Computer Vision Annotation Tool (CVAT), a free open source tool developed and made available by Intel ( 13 ). In total, 2,796 crickets were labelled.…”

Section: Methodsmentioning

confidence: 99%

Towards Machine Vision for Insect Welfare Monitoring and Behavioural Insights

et al. 2022

View full text Add to dashboard Cite

Machine vision has demonstrated its usefulness in the livestock industry in terms of improving welfare in such areas as lameness detection and body condition scoring in dairy cattle. In this article, we present some promising results of applying state of the art object detection and classification techniques to insects, specifically Black Soldier Fly (BSF) and the domestic cricket, with the view of enabling automated processing for insect farming. We also present the low-cost “Insecto” Internet of Things (IoT) device, which provides environmental condition monitoring for temperature, humidity, CO2, air pressure, and volatile organic compound levels together with high resolution image capture. We show that we are able to accurately count and measure size of BSF larvae and also classify the sex of domestic crickets by detecting the presence of the ovipositor. These early results point to future work for enabling automation in the selection of desirable phenotypes for subsequent generations and for providing early alerts should environmental conditions deviate from desired values.

show abstract

Section: Methodsmentioning

confidence: 99%

Towards Machine Vision for Insect Welfare Monitoring and Behavioural Insights

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The core images from Slovakia were cut into short segments along the long axis. Images were hand-annotated using the free online tool CVAT (Sekachev et al, 2020). Ring boundaries were annotated as a polyline then transformed into polygons by adding a buffer of 30 pixels in all directions from the original line.…”

Section: Training and Validation Datamentioning

confidence: 99%

Automation of tree-ring detection and measurements using deep learning

Poláček

Arizpe

Hüther

et al. 2022

Preprint

View full text Add to dashboard Cite

We present an implementable neural network-based automated detection and measurement of tree-ring boundaries from coniferous species.We trained our Mask R-CNN extensively on over 8,000 manually annotated rings. We assessed the performance of the trained model from our core processing pipeline on real world data.The CNN performed well, recognizing over 99% of ring boundaries (precision) and a recall value of 95% when tested on real world data. Additionally, we have implemented automatic measurements based on minimum distance between rings. With minimal editing for missed ring detections, these measurements were a 99% match with human measurements of the same samples.Our CNN is readily deployable through a Docker container and requires only basic command line skills. Application outputs include editable annotations which facilitate the efficient generation of ring-width measurements from tree-ring samples, an important source of environmental data.

show abstract

“…The acquired data were processed before machine learning (Figure 2). For this, an RGB representation of each acquisition was used to manually annotate each flower, leaf, or stem with polygons (Figure 1b) using the software Computer Vision Annotation Tool (CVAT) (version: server 1.1, core 2.0.1, canvas 2.0.1, UI 1.2.0) [49]. A minimal distance was kept from the outer edges of the samples during annotation to exclude background information and rule out border effects.…”

Section: Data Processingmentioning

confidence: 99%

Spectral-Based Classification of Plant Species Groups and Functional Plant Parts in Managed Permanent Grassland

et al. 2022

View full text Add to dashboard Cite

Grassland vegetation typically comprises the species groups grasses, herbs, and legumes. These species groups provide different functional traits and feed values. Therefore, knowledge of the botanical composition of grasslands can enable improved site-specific management and livestock feeding. A systematic approach was developed to analyze vegetation of managed permanent grassland using hyperspectral imaging in a laboratory setting. In the first step, hyperspectral images of typical grassland plants were recorded, annotated, and classified according to species group and plant parts, that is, flowers, leaves, and stems. In the second step, three different machine learning model types—multilayer perceptron (MLP), random forest (RF), and partial least squares discriminant analysis (PLS-DA)—were trained with pixel-wise spectral information to discriminate different species groups and plant parts in individual models. The influence of radiometric data calibration and specific data preprocessing steps on the overall model performance was also investigated. While the influence of proper radiometric calibration was negligible in our setting, specific preprocessing variants, including smoothening and derivation of the spectrum, were found to be beneficial for classification accuracy. Compared to extensively preprocessed data, raw spectral data yielded no statistically decreased performance in most cases. Overall, the MLP models outperformed the PLS-DA and RF models and reached cross-validation accuracies of 96.8% for species group and 88.6% for plant part classification. The obtained insights provide an essential basis for future data acquisition and data analysis of grassland vegetation.

show abstract

opencv/cvat: v1.1.0

Cited by 59 publications

References 0 publications

Towards Machine Vision for Insect Welfare Monitoring and Behavioural Insights

Towards Machine Vision for Insect Welfare Monitoring and Behavioural Insights

Automation of tree-ring detection and measurements using deep learning

Spectral-Based Classification of Plant Species Groups and Functional Plant Parts in Managed Permanent Grassland

Contact Info

Product

Resources

About