Previous research has indicated that preweaned dairy calves reared in pairs compared with individually have improved performance and indicators of animal welfare. One hundred and thirty Holstein female calves completed the trial, with eighty-five being allocated to paired housing and forty-five calves being allocated to individual housing. Daily live weight gain (DLWG), treatments and mortality were recorded throughout the preweaning period. Salivary cortisol, latency to feed and latency to approach a novel object were assessed at batching. There were no significant differences in DLWG, mortality and disease treatments between the average of the pair and the individually housed calves, although the pair-reared calves were quicker to approach the milk feed after batching and interacted more quickly with a novel object. The heaviest born calves within the pair had the highest DLWG from birth to weaning, with a higher percentage of calves approaching the novel object, compared with the lightest born calf within the pair. This study shows that calves within a pair may have significantly different performance and welfare during the preweaning period, with the heavier calf outperforming and displaying less fear and more exploratory behaviour than the lighter calf within a pair.
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http://dx.doi.org/10.3390/ani14111540 | DOI Listing |
Bioinformatics
January 2025
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China.
Motivation: Ensuring connectivity and preventing fractures in tubular object segmentation are critical for downstream analyses. Despite advancements in deep neural networks (DNNs) that have significantly improved tubular object segmentation, existing methods still face limitations. They often rely heavily on precise annotations, hindering their scalability to large-scale unlabeled image datasets.
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January 2025
NUS-ISS, National University of Singapore, Singapore 119615, Singapore.
Recognizing the action of plastic bag taking from CCTV video footage represents a highly specialized and niche challenge within the broader domain of action video classification. To address this challenge, our paper introduces a novel benchmark video dataset specifically curated for the task of identifying the action of grabbing a plastic bag. Additionally, we propose and evaluate three distinct baseline approaches.
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January 2025
School of Mechanical and Electrical Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China.
Unsupervised Domain Adaptation for Object Detection (UDA-OD) aims to adapt a model trained on a labeled source domain to an unlabeled target domain, addressing challenges posed by domain shifts. However, existing methods often face significant challenges, particularly in detecting small objects and over-relying on classification confidence for pseudo-label selection, which often leads to inaccurate bounding box localization. To address these issues, we propose a novel UDA-OD framework that leverages scale consistency (SC) and Temporal Ensemble Pseudo-Label Selection (TEPLS) to enhance cross-domain robustness and detection performance.
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December 2024
Department of Computer Science & Artificial Intelligence, Jeonbuk National University, Jeonju-si 54896, Republic of Korea.
Recently, computer vision methods have been widely applied to agricultural tasks, such as robotic harvesting. In particular, fruit harvesting robots often rely on object detection or segmentation to identify and localize target fruits. During the model selection process for object detection, the average precision (AP) score typically provides the de facto standard.
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December 2024
School of Coal Engineering, Shanxi Datong University, Datong 037000, China.
In the complex environment of fully mechanized mining faces, the current object detection algorithms face significant challenges in achieving optimal accuracy and real-time detection of mine personnel and safety helmets. This difficulty arises from factors such as uneven lighting conditions and equipment obstructions, which often lead to missed detections. Consequently, these limitations pose a considerable challenge to effective mine safety management.
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