结合卷积神经网络的自适应亮度调节对笼中母鸡头部状态的精细检测

IF 2.2 2区 农林科学 Q2 AGRICULTURAL ENGINEERING International Journal of Agricultural and Biological Engineering Pub Date : 2023-01-01 DOI:10.25165/j.ijabe.20231603.7507
Jia Chen, Qi’an Ding, Wen Yao, Mingxia Shen, Longshen Liu
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引用次数: 0

摘要

及时发现和跟踪堆垛笼中的异常母鸡,对于精准治疗和消灭患病个体具有重要意义。笼养母鸡的头部特征被用来克服笼子和羽毛阻塞造成的观察困难,但仍然难以识别相似的头部状态。为了解决这一问题,采用自适应亮度调节与卷积神经网络(FBA-CNN)相结合的方法,开发了一种细粒度检测笼养母鸡头部状态的方法。基于网格区域的CNN (R-CNN)是一种卷积神经网络(CNN),采用挤压与激励(SE)和深度过度参数化卷积(DO-Conv)对其进行优化,以检测笼中的层头,并将其精确切割为单头图像。通过基于SE-Resnet50的深度卷积神经网络对每张单头图像的亮度进行自适应调整和分类。最后回归到原始图像,利用坐标映射实现多目标检测。结果表明:优化后网格R-CNN的层头检测AP@0.5为0.947,SE-Resnet50的分类准确率为0.749,F1得分为0.637,FBA-CNN的mAP@0.5为0.846。综上所述,该自动化方法可以准确识别层笼中不同的层头状态,为后续研究呼吸困难、恶病质等异常行为提供依据。关键词:网格R-CNN,挤压激励,深度过参数化卷积,自适应亮度调节,细粒度检测DOI: 10.25165/ j.j ijabe.20231603.7507引用本文:陈杰,丁庆安,姚伟,沈明霞,刘丽生。基于自适应亮度调节结合卷积神经网络的母鸡头部状态细粒度检测。农业与生物工程学报,2023;16(): 16(3): 208-216。
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Fine-grained detection of caged-hen head states using adaptive Brightness Adjustment in combination with Convolutional Neural Networks
Timely identification and tracking of abnormal hens in stacked cages are of great significance for precision treatment and the elimination of sick individuals. The head features of the caged-hens are used to overcome observation difficulties caused by the cage and feathers blocking, but it is still hard to identify similar head states. To solve this problem, a fine-grained detection of caged-hens head states was developed using adaptive Brightness Adjustment in combination with Convolutional Neural Networks (FBA-CNN). Grid Region-based CNN (R-CNN), a convolution neural network (CNN), was optimized with the Squeeze-and-Excitation (SE) and Depthwise Over-parameterized Convolutional (DO-Conv) to detect layer heads from cages and to accurately cut them as single-head images. The brightness of each single-head image was adjusted adaptively and classified through the deep convolution neural network based on SE-Resnet50. Finally, we returned to the original image to realize multi-target detection with coordinate mapping. The results showed that the AP@0.5 of layer head detection using the optimized Grid R-CNN was 0.947, the accuracy of classification with SE-Resnet50 was 0.749, the F1 score was 0.637, and the mAP@0.5 of FBA-CNN was 0.846. In summary, this automated method can accurately identify different layer head states in layer cages to provide a basis for follow-up studies of abnormal behavior including dyspnea and cachexia. Keywords: Grid R-CNN, squeeze-and-excitation, Depthwise Over-parameterized Convolutional, adaptive brightness adjustment, fine-grained detection DOI: 10.25165/j.ijabe.20231603.7507 Citation: Chen J, Ding Q A, Yao W, Shen M X, Liu L S. Fine-grained detection of caged-hens head states using adaptive Brightness Adjustment in combination with Convolutional Neural Networks. Int J Agric & Biol Eng, 2023; 16(): 16(3): 208–216.
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来源期刊
CiteScore
4.30
自引率
12.50%
发文量
88
审稿时长
24 weeks
期刊介绍: International Journal of Agricultural and Biological Engineering (IJABE, https://www.ijabe.org) is a peer reviewed open access international journal. IJABE, started in 2008, is a joint publication co-sponsored by US-based Association of Agricultural, Biological and Food Engineers (AOCABFE) and China-based Chinese Society of Agricultural Engineering (CSAE). The ISSN 1934-6344 and eISSN 1934-6352 numbers for both print and online IJABE have been registered in US. Now, Int. J. Agric. & Biol. Eng (IJABE) is published in both online and print version by Chinese Academy of Agricultural Engineering.
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