Building an artificial vision system of an agricultural robot based on the DarkNet system

Доррер, М. Г.; Popov, A. A.; Tolmacheva, A. E.

doi:10.1088/1755-1315/548/3/032032

Cited by 7 publications

(3 citation statements)

References 6 publications

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“…American scholar Santos defined physical fitness as “physique, sports adaptability, and organ performance.” American scholar Stan Howe pointed out that physical adaptability is human adaptability and mental influence, which can meet the needs of modern life, including physical function, health, mental maintenance, and improvement [ 6 ]. Dorrer et al designed a basketball shooting robot based on the vision system by combining the control system and the computer vision system and equipped with a shooting arm, webcam, and other devices [ 7 ]. When shooting, people can use the visual system, special structure, and mobility to find the best angle to shoot the basket and use the laser pointer to correct and locate the shooting angle to improve the shooting accuracy.…”

Section: Related Workmentioning

confidence: 99%

Design of the Physical Fitness Evaluation Information Management System of Sports Athletes Based on Artificial Intelligence

Liu

Zhu

2022

Computational Intelligence and Neuroscience

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With the rapid development of science and technology in recent years, more and more researchers began to explore the basic disciplines of sports, namely, biomechanics and physiology, and further update and improve the traditional sports training theory. However, the developed countries, i.e., Europe and the United States, have dedicatedly worked on the effective role of physical training particularly in middle-class farmers in sports powers. They have realized that the continuous improvement of the physical training evaluation system is directly related to the reforming in the evaluation and monitoring methods of physical training. At the same time, there is a demand for athletes’ physical fitness evaluation, which requires a large amount of data support during the evaluation. These data collection and analysis are difficult and consume a lot of manpower. Therefore, this paper uses artificial intelligence technology to design the sports athletes’ physical fitness evaluation information management system to improve the comprehensiveness of system data collection and the accuracy of data analysis. Through the physical attenuation calculation and physical analysis of the system developed in this paper, data accuracy is high. The coach can make a training plan according to the physical consumption of athletes in sports training or competition, which can greatly improve the ability of athletes.

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Section: Related Workmentioning

confidence: 99%

Design of the Physical Fitness Evaluation Information Management System of Sports Athletes Based on Artificial Intelligence

Liu

Zhu

2022

Computational Intelligence and Neuroscience

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“…The labels were used to train a model using the darknet neural network framework [67] for generating custom models. This methodology was selected because of its successful implementations in the agricultural industry and with UAS datasets [68,69]. YOLOv3 [70] was used to explore the possibility of detecting the Geum radiatum plants on the UAS aerial imagery.…”

Section: Semi-automated Detection With Computer Visionmentioning

confidence: 99%

Efficient Drone-Based Rare Plant Monitoring Using a Species Distribution Model and AI-Based Object Detection

Reckling

Mitášová

Wegmann

et al. 2021

Drones

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Monitoring rare plant species is used to confirm presence, assess health, and verify population trends. Unmanned aerial systems (UAS) are ideal tools for monitoring rare plants because they can efficiently collect data without impacting the plant or endangering personnel. However, UAS flight planning can be subjective, resulting in ineffective use of flight time and overcollection of imagery. This study used a Maxent machine-learning predictive model to create targeted flight areas to monitor Geum radiatum, an endangered plant endemic to the Blue Ridge Mountains in North Carolina. The Maxent model was developed with ten environmental layers as predictors and known plant locations as training data. UAS flight areas were derived from the resulting probability raster as isolines delineated from a probability threshold based on flight parameters. Visual analysis of UAS imagery verified the locations of 33 known plants and discovered four previously undocumented occurrences. Semi-automated detection of plant species was explored using a neural network object detector. Although the approach was successful in detecting plants in on-ground images, no plants were identified in the UAS aerial imagery, indicating that further improvements are needed in both data acquisition and computer vision techniques. Despite this limitation, the presented research provides a data-driven approach to plan targeted UAS flight areas from predictive modeling, improving UAS data collection for rare plant monitoring.

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“…The authors of this work also investigated the possibilities of solving the problems of monitoring production sites and tracking vehicles using computer vision tools in their works [6], [7] and [8].…”

Section: Introductionmentioning

confidence: 99%

Application of convolutional networks of the YOLO architecture for calculating free parking spaces in an arbitrary parking lot

Alekhina,

Korolev,

Dorrer

2023

E3S Web of Conf.

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The paper describes the solution of the problem of automatic fixation of the availability of free parking spaces in parking lots based on the video stream of the surveillance camera. The problem was solved using a combination of the YOLOv7 model for detecting objects of interest of the “car” type, trained on a specially created training sample and grouping of objects using the k-means algorithm. The proposed solution has shown satisfactory accuracy and suitability for practical use in conditions of significant optical interference – daily light fluctuations and precipitation for calculating free parking spaces by the model without retraining it for various parking lots

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Building an artificial vision system of an agricultural robot based on the DarkNet system

Cited by 7 publications

References 6 publications

Design of the Physical Fitness Evaluation Information Management System of Sports Athletes Based on Artificial Intelligence

Design of the Physical Fitness Evaluation Information Management System of Sports Athletes Based on Artificial Intelligence

Efficient Drone-Based Rare Plant Monitoring Using a Species Distribution Model and AI-Based Object Detection

Application of convolutional networks of the YOLO architecture for calculating free parking spaces in an arbitrary parking lot

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