Jia Chen, Qi’an Ding, Wen Yao, Mingxia Shen, Longshen Liu
{"title":"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","authors":"Jia Chen, Qi’an Ding, Wen Yao, Mingxia Shen, Longshen Liu","doi":"10.25165/j.ijabe.20231603.7507","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":null,"pages":null},"PeriodicalIF":2.2000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Fine-grained detection of caged-hen head states using adaptive Brightness Adjustment in combination with Convolutional Neural Networks\",\"authors\":\"Jia Chen, Qi’an Ding, Wen Yao, Mingxia Shen, Longshen Liu\",\"doi\":\"10.25165/j.ijabe.20231603.7507\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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. 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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.
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.