IATEFF-YOLO:关注夜间奶牛上架检测

IF 4.4 1区 农林科学 Q1 AGRICULTURAL ENGINEERING
De Li , Baisheng Dai , Yanxing Li , Peng Song , Xin Dai , Yongqiang He , Huixin Liu , Yang Li , Weizheng Shen
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引用次数: 0

摘要

上座行为是发情期奶牛的一个重要特征。实时、准确地检测奶牛上座行为可缩短产犊到受孕的时间,提高奶牛场的经济效益。奶牛上座行为多发生在夜间,奶牛与摄像机之间的距离不同会导致监控图像的尺度发生剧烈变化,从而影响奶牛上座行为的检测。现有方法无法有效解决这些难题。为了解决这些难题,本研究利用摄像机收集了 9392 张集约化养殖条件下荷斯坦奶牛上座行为的图像,并提出了一种 IATEFF-YOLO 方法,该方法更适合于夜间奶牛上座行为的检测,以及奶牛与摄像机之间不同距离造成的监控图像尺度的剧烈变化。IATEFF-YOLO 包括一个照明自适应变换器(IAT)和一个高效的特征融合检测器。IAT 可增强夜间低照度图像,从而丰富奶牛的骑乘特征,便于随后检测奶牛的骑乘行为。高效特征融合检测器 EFF-YOLO 增强了特征融合能力,并进一步使检测器能够适应监控图像中因奶牛与摄像机之间的距离不同而导致的尺度急剧变化。IATEFF-YOLO 的平均精确度达到 99.3%,检测速度达到 102.0 f/s。与现有方法相比,IATEFF-YOLO 在夜间和因奶牛与摄像机之间的距离不同而导致监控图像尺度急剧变化的情况下实现了更高的检测精度和更快的检测速度。IATEFF-YOLO 可帮助牧场饲养人员实现对奶牛发情的全天候监测,从而提高发情检测效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
IATEFF-YOLO: Focus on cow mounting detection during nighttime

Mounting behaviour is an important characteristic of cows during oestrus. Real-time and accurate detection of cow mounting behaviour can shorten the calving-to-conception period and increase the economic benefits for dairy farms. Cow mounting behaviour occurs more often at night, and drastic scale changes in surveillance images caused by different distances between cows and camera, influence the detection of cow mounting. Existing methods are unable to address these challenges effectively. To address these challenges, this study collected 9392 images of Holstein cow mounting behaviour under intensive farming conditions using cameras and proposed an IATEFF-YOLO that is more suitable for cow mounting behaviour detection at nighttime and drastic scale changes in surveillance images caused by different distances between cows and camera. IATEFF-YOLO comprises an Illumination Adaptive Transformer (IAT) and an efficient feature fusion detector. The IAT enhances low-light images at night to enrich the cow mounting features, facilitating the subsequent detection of mounting behaviour. The efficient feature fusion detector, EFF-YOLO, enhances the feature fusion capability and further enable the detector to adapt to drastic scale changes in surveillance images caused by different distances between cows and camera. IATEFF-YOLO achieved a mean Average Precision of 99.3% and a detection speed of 102.0 f/s on test set. Compared with existing methods, IATEFF-YOLO achieved higher detection accuracy and faster detection speed during nighttime and drastic scale changes in surveillance images caused by different distances between cows and camera. IATEFF-YOLO can assist ranch breeders in achieving round-the-clock monitoring of cow oestrus, thereby enhancing oestrus detection efficiency.

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来源期刊
Biosystems Engineering
Biosystems Engineering 农林科学-农业工程
CiteScore
10.60
自引率
7.80%
发文量
239
审稿时长
53 days
期刊介绍: Biosystems Engineering publishes research in engineering and the physical sciences that represent advances in understanding or modelling of the performance of biological systems for sustainable developments in land use and the environment, agriculture and amenity, bioproduction processes and the food chain. The subject matter of the journal reflects the wide range and interdisciplinary nature of research in engineering for biological systems.
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