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StaticPigDet: Accuracy Improvement of Static Camera-Based Pig Monitoring Using Background and Facility Informationopen access

Authors
Son, SeungwookAhn, HanseBaek, HwapyeongYu, SeunghyunSuh, YooilLee, SungjuChung, YongwhaPark, Daihee
Issue Date
11월-2022
Publisher
MDPI
Keywords
pig detection; image processing; deep learning; video monitoring; static camera; background; facility; occlusion
Citation
SENSORS, v.22, no.21
Indexed
SCIE
SCOPUS
Journal Title
SENSORS
Volume
22
Number
21
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/146519
DOI
10.3390/s22218315
ISSN
1424-8220
Abstract
The automatic detection of individual pigs can improve the overall management of pig farms. The accuracy of single-image object detection has significantly improved over the years with advancements in deep learning techniques. However, differences in pig sizes and complex structures within pig pen of a commercial pig farm, such as feeding facilities, present challenges to the detection accuracy for pig monitoring. To implement such detection in practice, the differences should be analyzed by video recorded from a static camera. To accurately detect individual pigs that may be different in size or occluded by complex structures, we present a deep-learning-based object detection method utilizing generated background and facility information from image sequences (i.e., video) recorded from a static camera, which contain relevant information. As all images are preprocessed to reduce differences in pig sizes. We then used the extracted background and facility information to create different combinations of gray images. Finally, these images are combined into different combinations of three-channel composite images, which are used as training datasets to improve detection accuracy. Using the proposed method as a component of image processing improved overall accuracy from 84% to 94%. From the study, an accurate facility and background image was able to be generated after updating for a long time that helped detection accuracy. For the further studies, improving detection accuracy on overlapping pigs can also be considered.
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과학기술대학 (컴퓨터융합소프트웨어학과)
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