ID-APM: Inverse Disparity-Guided Annealing Point Matching Approach for Robust ROI Localization in Blurred Thermal Images of Sika Deer

Caocan Zhu, Ye Mu, Yu Sun, He Gong, Ying Guo, Juanjuan Fan, Shijun Li, Zhipeng Li and Tianli Hu ()
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Caocan Zhu: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Ye Mu: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Yu Sun: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
He Gong: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Ying Guo: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Juanjuan Fan: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Shijun Li: School of Electronics and Information Engineering, Wuzhou University, Wuzhou 543002, China
Zhipeng Li: College of Animal Science and Technology, Jilin Agricultural University, Changchun 130118, China
Tianli Hu: College of Information Technology, Jilin Agricultural University, Changchun 130118, China

Agriculture, 2025, vol. 15, issue 19, 1-22

Abstract: Non-contact, automated health monitoring is a cornerstone of modern precision livestock farming, crucial for enhancing animal welfare and productivity. Infrared thermography (IRT) offers a powerful, non-invasive means to assess physiological status. However, its practical use on farms is limited by a key challenge: accurately locating regions of interest (ROIs), like the eyes and face, in the blurry, low-resolution thermal images common in farm settings. To solve this, we developed a new framework called ID-APM, which is designed for robust ROI registration in agriculture. Our method uses a trinocular system and our RAP-CPD algorithm to robustly match features and accurately calculate the target’s 3D position. This 3D information then enables the precise projection of the ROI’s location onto the ambiguous thermal image through inverse disparity estimation, effectively overcoming errors caused by image blur and spectral inconsistencies. Validated on a self-built dataset of farmed sika deer, the ID-APM framework demonstrated exceptional performance. It achieved a remarkable overall accuracy of 96.95% and a Correct Matching Ratio (CMR) of 99.93%. This research provides a robust and automated solution that effectively bypasses the limitations of low-resolution thermal sensors, offering a promising and practical tool for precision health monitoring, early disease detection, and enhanced management of semi-wild farmed animals like sika deer.

Keywords: animal monitor; ID-APM; RAP-CPD; image match (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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