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http://dx.doi.org/10.9717/kmms.2022.25.4.583

Accuracy Improvement of Pig Detection using Image Processing and Deep Learning Techniques on an Embedded Board  

Yu, Seunghyun (Department of Computer Convergence Software, Korea University)
Son, Seungwook (Department of Computer Convergence Software, Korea University)
Ahn, Hanse (Department of Computer Convergence Software, Korea University)
Lee, Sejun (Department of Computer Convergence Software, Korea University)
Baek, Hwapyeong (Department of Computer Convergence Software, Korea University)
Chung, Yongwha (Department of Computer Convergence Software, Korea University)
Park, Daihee (Department of Computer Convergence Software, Korea University)
Publication Information
Journal of Korea Multimedia Society / v.25, no.4, 2022 , pp. 583-599 More about this Journal
Abstract
Although the object detection accuracy with a single image has been significantly improved with the advance of deep learning techniques, the detection accuracy for pig monitoring is challenged by occlusion problems due to a complex structure of a pig room such as food facility. These detection difficulties with a single image can be mitigated by using a video data. In this research, we propose a method in pig detection for video monitoring environment with a static camera. That is, by using both image processing and deep learning techniques, we can recognize a complex structure of a pig room and this information of the pig room can be utilized for improving the detection accuracy of pigs in the monitored pig room. Furthermore, we reduce the execution time overhead by applying a pruning technique for real-time video monitoring on an embedded board. Based on the experiment results with a video data set obtained from a commercial pig farm, we confirmed that the pigs could be detected more accurately in real-time, even on an embedded board.
Keywords
Real-Time Video Monitoring; Video Object Detection; Pig Detection; Image Processing; Deep Learning; YOLO;
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Times Cited By KSCI : 1  (Citation Analysis)
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