MFSnet:用于密集环境下鸡计数的多尺度特征筛选网络。

IF 1.7 3区 农林科学 Q2 AGRICULTURE, DAIRY & ANIMAL SCIENCE
G Ma, Z Xiao, F Yuan, E Sun, S Chen, J Liu, B He
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引用次数: 0

摘要

1. 基于机器视觉的鸡计数是一种高效的方法。尽管如此,在高繁殖密度的情况下,捕获图像中的鸡经常相互重叠。本研究解决了在人口密集的环境中准确计算散养鸡舍中鸡的数量的挑战。它提出了一个专门为密集场景设计的小鸡计数网络,即MFSnet.2。该研究提取了多尺度特征图,并通过特征筛选模块(FSM)在融合阶段对其进行处理。该模块生成的特征图丰富了不同尺度的特征,增强了信息,从而增强了网络准确识别鸡的能力。收集数据集并标记并命名为Chicken2023。它由550幅图像组成,总共包含49747只鸡。为了验证其有效性,将其与现有的计数算法进行了比较。基于Chicken2023数据集的实验结果表明,该方法达到了较好的计数性能水平。其平均绝对误差(MAE)为2.7,均方根误差(RMSE)为3.6。当与表现最好的网络并置时,它显示出显著的改善,MAE降低了6.25%,rmse降低了6.26%。本研究提出的网络模型能够准确识别密集环境下的鸡只数量,提高了家禽养殖效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MFSnet: a multi-scale feature screening network for chicken counting in dense environments.

1. Machine-vision-based chicken counting is a highly efficient approach. Nonetheless, in scenarios with high breeding densities, chickens in the captured images frequently overlap with one another. This research addressed the challenge of accurately counting chickens within a free-range chicken coop in densely environments. It proposes a chicken-counting network specifically designed for dense scenarios, namely MFSnet.2. The study extracted multi-scale feature maps and subjected them to processing during the fusion stage via a Feature Screening Module (FSM). This module generated feature maps that were richly endowed with features from diverse scales to enhance information, thereby augmenting the network's capacity to accurately identify chickens.3. The dataset was collected and labelled and denominated as Chicken2023. It consisted of 550 images, which, in aggregate, encompassed a total of 49 747 chickens. To validate its efficacy, it was compared with extant counting algorithms. The experimental findings derived from the Chicken2023 dataset illustrated that this method attained a better counting performance level. It achieved a mean absolute error (MAE) of 2.7 and a root mean square error (RMSE) of 3.6. When juxtaposed with the top-performing network, it showed a notable improvement, with a 6.25% reduction in MAE and a 6.26% reduction in RMSE.4. The network model proposed in this study accurately recognised the number of chickens in dense environments and improved the efficiency of poultry farming.

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来源期刊
British Poultry Science
British Poultry Science 农林科学-奶制品与动物科学
CiteScore
3.90
自引率
5.00%
发文量
88
审稿时长
4.5 months
期刊介绍: From its first volume in 1960, British Poultry Science has been a leading international journal for poultry scientists and advisers to the poultry industry throughout the world. Over 60% of the independently refereed papers published originate outside the UK. Most typically they report the results of biological studies with an experimental approach which either make an original contribution to fundamental science or are of obvious application to the industry. Subjects which are covered include: anatomy, embryology, biochemistry, biophysics, physiology, reproduction and genetics, behaviour, microbiology, endocrinology, nutrition, environmental science, food science, feeding stuffs and feeding, management and housing welfare, breeding, hatching, poultry meat and egg yields and quality.Papers that adopt a modelling approach or describe the scientific background to new equipment or apparatus directly relevant to the industry are also published. The journal also features rapid publication of Short Communications. Summaries of papers presented at the Spring Meeting of the UK Branch of the WPSA are published in British Poultry Abstracts .
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