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Precise detection of the object is critical for posture recognition of pigs. The posture detection has been a great interest for researchers, as the detection are linked with the health and welfare of the livestock. Thus, the images obtained from different angles and distance making the posture detection a challenging task. In this paper, we propose an effective posture detection with the combination of (YOLO) and holistically-nested edge detection (HED) to address the issues. The proposed model analysis whether the image along with the Holistically edge detection and machine learning approach could be utilities to identify the posture detection of the pig such as sitting, lying and standing postures. Two approaches that included YOLO with RGB images and Yolov3 with Holistically-Nested Edge Detection (HED) images were included to compare the results. Data from single farm at different time were used for training and validation of the proposed models. The experiment result confirms that the YOLO with HED images was able to detect the posture of the pigs with high accuracy of Mean average precision greater than 0.94%.
"Optimized HED- YOLO algorithm for posture detection of pigs", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.6, Issue 8, page no.18 - 23, August-2021, Available :http://www.ijrti.org/papers/IJRTI2108004.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator