Experiments with Neural Net Object Detection System YOLO on Small Training Datasets for Intelligent Robotics

Kulik, Sergey; Shtanko, A. N.

doi:10.1007/978-3-030-33491-8_19

Cited by 21 publications

(7 citation statements)

References 14 publications

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“…If it is necessary to use several information sources at the same time for data extraction, the second method is usually not used. Automate data collection during the development of software can be through the use of agent technologies (software agents) [5,6]. Software agent is a computer program that acts on behalf of a user on demand or according to a schedule.…”

Section: Collecting Data About Policies On the Internetmentioning

confidence: 99%

Graph Visualization of the Characteristics of Complex Objects on the Example of the Analysis of Politicians

Ulizko¹,

Antonov²,

Artamonov

et al. 2020

Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2

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The paper considers the task of analyzing complex interconnected objects using graph construction. There is no unified tool for constructing graphs. Some solutions can build graphs limited by the number of nodes, while others do not visually display data. The Gephi application was used to construct graphs for the research. Gephi has great functionality for building and analyzing graphs. The subject of research is a politician with a certain set of characteristics. In the paper an algorithm that enables to automate data collection on politicians was developed. One of the main methods of data collecting on the Internet is web scraping. Web scraping software may access the World Wide Web directly using the HTTP, or through a web browser. While web scraping can be done manually by a software user, the term typically refers to automated processes implemented using a software agent. The data was necessary for constructing graphs and their analysis. The use of graphs enables to see various types of relationships, including mediate. This methodology enables to change the attitude towards the analysis of multidi-mensional objects.

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Section: Collecting Data About Policies On the Internetmentioning

confidence: 99%

Graph Visualization of the Characteristics of Complex Objects on the Example of the Analysis of Politicians

Ulizko¹,

Antonov²,

Artamonov

et al. 2020

Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2

View full text Add to dashboard Cite

show abstract

“…If it is necessary to use several information sources at the same time for data extraction, the second method is usually not used. Automate data collection during the development of software can be through the use of agent technologies (software agents) [7,8]. Software agent is a computer program that acts on behalf of a user on demand or according to a schedule.…”

Section: Collecting Data About Politicians On the Internetmentioning

confidence: 99%

“…Information about politicians is available on various Internet sources. As an exam-ple, information from such sources as VoteSmart and Govtrack is considered [7,8]. They have a complex structure, so it is impossible to use third-party software.…”

Section: Collecting Data About Politicians On the Internetmentioning

confidence: 99%

Visualization of Graph-based representations for analyzing related multidimensional objects

Ulizko¹,

Antonov²,

Artamonov³

et al. 2020

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The paper considers the task of analyzing complex interconnected objects using graphs. The subject of the research is such multidimensional object as a "politician". The paper presents the main methods of visualizing multidimensional data and the choice of data analysis is justified using graphs. An algorithm that enables to automate data collection on politicians was developed. One of the main methods of data collecting on the Internet is web scraping. Web scraping software may access the World Wide Web directly using the HTTP, or through a web browser. While web scraping can be done manually by a software user, the term typically refers to automated processes implemented using a software agent. The process of transition from an object of the "personality" type to its graph representation with various types of nodes and edges is described. The use of graphs enables to see various types of relationships, including mediate. The features of the used Gephi software for data analysis are presented. This methodology enables to change the attitude towards the analysis of multidimensional objects.

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“…Such solutions can seamlessly integrate into existing surface defect inspection processes, enhancing efficiency and circumventing bottlenecks inherent in traditional inspection methods [14]. Nevertheless, achieving success requires that CV architectures adhere to a stringent set of deployment prerequisites, which can vary across different sectors within the manufacturing industry [15]. In the majority of applications, the objective extends beyond merely identifying individual defects; it also encompasses the identification of multiple defects and their specific spatial details [16].…”

Section: Introductionmentioning

confidence: 99%

“…These models have also played a role in detecting and monitoring wildlife, assisting in pinpointing endangered animals, and bolstering efforts in biodiversity preservation and habitat management [41]. Furthermore, YOLO has found extensive applications in robotics [15] and in object detection via drones [42].…”

Section: Introductionmentioning

confidence: 99%

An Improved YOLOv8 to Detect Moving Objects

Safaldin,

Zaghden,

Mejdoub

2024

IEEE Access

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Deep learning has revolutionized object detection, with YOLO (You Only Look Once) leading in real-time accuracy. However, detecting moving objects in visual streams presents distinct challenges. This paper proposes a refined YOLOv8 object detection model, emphasizing motion-specific detections in varied visual contexts. Through tailored preprocessing and architectural adjustments, we heighten the model's sensitivity to object movements. Rigorous testing against KITTI, LASIESTA, PESMOD, and MOCS benchmark datasets revealed that the modified YOLOv8 outperforms the state-of-the-art detection models, especially in environments with significant movement. Specifically, our model achieved an accuracy of 90%, a mean Average Precision (mAP) of 90%, and maintained a processing speed of 30 frames per second (FPS), with an Intersection over Union (IoU) score of 80%. This paper offers a detailed insight into object trajectories, proving invaluable in areas like security, traffic management, and film analysis where motion understanding is critical. As the importance of dynamic scene interpretation grows in artificial intelligence and computer vision, the proposed enhanced YOLOv8 detection model highlights the potential of specialized object detection and underscores the significance of our findings in the evolving field of object detection.

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Experiments with Neural Net Object Detection System YOLO on Small Training Datasets for Intelligent Robotics

Cited by 21 publications

References 14 publications

Graph Visualization of the Characteristics of Complex Objects on the Example of the Analysis of Politicians

Graph Visualization of the Characteristics of Complex Objects on the Example of the Analysis of Politicians

Visualization of Graph-based representations for analyzing related multidimensional objects

An Improved YOLOv8 to Detect Moving Objects

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