KFU. Card publication. Infrared video-based object recognition for agricultural research field uses

KAZAN
FEDERAL UNIVERSITY

- RUS
- 中文
- ES

INFRARED VIDEO-BASED OBJECT RECOGNITION FOR AGRICULTURAL RESEARCH FIELD USES

Form of presentation

Articles in international journals and collections

Year of publication

2024

Язык

английский

Authors, employees of KFU

Kugurakova Vlada Vladimirovna, author

Sergunin Igor Dmitrievich, author

Authors, students KFU

Guzaerov Damir Alfredovich, author

Bibliographic description in the original language

Guzaerov D.A., Sergunin I.D., Kugurakova V.V. Infrared video-based object recognition for agricultural research field uses //BIO Web of Conferences. - 2024. - Vol.141. - Art. №01025.

Annotation

The paper presents a study of an object recognition system based on infrared (IR) video stream using the YOLOv8 model. The aim of the work was to develop and evaluate the performance of a computer vision algorithm for analysing infrared images under various conditions including low light. While the use of RGB images can provide rich information in normal conditions, it becomes inefficient to use a visible spectrum camera in total darkness or in the presence of clutter. The study was based on an extensive dataset containing about 176,000 annotated frames from an infrared camera. The YOLOv8 model was adapted and trained to recognise 15 classes of objects, including people, animals and different types of vehicles. The results showed high performance of the model in recognising the most represented classes in the dataset, such as cars, people with F1-score up to 0.8. However, the problem of unbalanced classes was identified, leading to lower recognition accuracy for rarely seen objects. The study demonstrates the potential of using infrared cameras in computer vision tasks and opens new directions for further developments in the field of object recognition in limited visibility conditions. The results of the work may find application in a wide range of disciplines, including medicine, agriculture and environmental monitoring.

Keywords

recognition, infrared, IR, video stream, YOLOv8, computer vision, synthetic dataset.

The name of the journal

BIO Web of Conferences

URL

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216758944&doi=10.1051%2fbioconf%2f202414101025&partnerID=40&md5=af89c1a9826c8422b9736ca93234e932

Please use this ID to quote from or refer to the card

https://repository.kpfu.ru/eng/?p_id=311540&p_lang=2

Full metadata record

Field DC	Value	Language
dc.contributor.author	Kugurakova Vlada Vladimirovna	ru_RU
dc.contributor.author	Sergunin Igor Dmitrievich	ru_RU
dc.contributor.author	Guzaerov Damir Alfredovich	ru_RU
dc.date.accessioned	2024-01-01T00:00:00Z	ru_RU
dc.date.available	2024-01-01T00:00:00Z	ru_RU
dc.date.issued	2024	ru_RU
dc.identifier.citation	Guzaerov D.A., Sergunin I.D., Kugurakova V.V. Infrared video-based object recognition for agricultural research field uses //BIO Web of Conferences. - 2024. - Vol.141. - Art. №01025.	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/eng/?p_id=311540&p_lang=2	ru_RU
dc.description.abstract	BIO Web of Conferences	ru_RU
dc.description.abstract	The paper presents a study of an object recognition system based on infrared (IR) video stream using the YOLOv8 model. The aim of the work was to develop and evaluate the performance of a computer vision algorithm for analysing infrared images under various conditions including low light. While the use of RGB images can provide rich information in normal conditions, it becomes inefficient to use a visible spectrum camera in total darkness or in the presence of clutter. The study was based on an extensive dataset containing about 176,000 annotated frames from an infrared camera. The YOLOv8 model was adapted and trained to recognise 15 classes of objects, including people, animals and different types of vehicles. The results showed high performance of the model in recognising the most represented classes in the dataset, such as cars, people with F1-score up to 0.8. However, the problem of unbalanced classes was identified, leading to lower recognition accuracy for rarely seen objects. The study demonstrates the potential of using infrared cameras in computer vision tasks and opens new directions for further developments in the field of object recognition in limited visibility conditions. The results of the work may find application in a wide range of disciplines, including medicine, agriculture and environmental monitoring.	ru_RU
dc.language.iso	ru	ru_RU
dc.subject	recognition	ru_RU
dc.subject	infrared	ru_RU
dc.subject	IR	ru_RU
dc.subject	video stream	ru_RU
dc.subject	YOLOv8	ru_RU
dc.subject	computer vision	ru_RU
dc.subject	synthetic dataset.	ru_RU
dc.title	Infrared video-based object recognition for agricultural research field uses	ru_RU
dc.type	Articles in international journals and collections	ru_RU