Hatching Egg Image Segmentation Based on Dense Blocks and the Hierarchical Sampling of Pixels
Keywords:
Deep learning, Semantic segmentation, Dense Block, Hierarchical sampling strategy, Hatching eggsAbstract
Fertility detection of hatching eggs is crucial in the manufacturing of vaccines. For hatching egg images, the segmentation results of blood vessels, cracks and air chambers are important for detecting the fertility of hatching eggs. In this paper, we propose an image segmentation method based on dense blocks and the hierarchical sampling of pixels. Dense blocks are used instead of the traditional layer-by-layer structure to improve efficiency and model robustness. The hierarchical sampling of pixels uses small batch sampling to add diversity during batch updates, which can accelerate learning. The sampled features are sparsely arranged and classified using an Multi-layer Perceptron(MLP), which can introduce complex nonlinear predictors and improve accuracy. The experimental results show that the MIoU reaches 90.5%. The proposed method can significantly improve the segmentation performance.
References
. Treanor J J, Campbell J D, Zangwill K M, Rowe T and Wolff M. “Safety and Immunogenicity of an Inactivated Subvirion Influenza A (H5N1) Vaccine.” New England Journal of Medicine, vol.354, pp. 1343-1351, 2006.
. Romanoff A L and Frank K. “High Frequency Conductivity and Dielectric Effect of Fresh Fertile and Infertile Hens'Eggs.” Experimental Biology & Medicine, vol.47, pp.527-530, 1941.
. Romanoff A L. “EVALUATION OF FUTURE PERISHABILITY OF INTACT FRESH EGGS BY RADIO‐FREQUENCY CONDUCTIVITY.” Journal of Food Science, vol.14, pp. 310-313, 1949.
. Sinha D N, Johnston R G, Grace W K and Lemanski C. “Acoustic Resonances in Chicken Eggs.” Biotechnology Progress, vol.8, pp.240-243, 1992.
. Coucke P M, Room G M, Decuypere E M and De Baerdemaeker J G. “Monitoring Embryo Development in Chicken Eggs using Acoustic Resonance Analysis.” Biotechnology Progress, vol.13, pp.474-478, 1997.
. Cain J R, Abbott U K and Rogallo V L. “Heart rate of the developing chick embryo.” Proceedings of the Society for Experimental Biology & Medicine Society for Experimental Biology & Medicine, vol.126, pp.507-510, 1967.
. Suzuki Y, Musashi H and Tazawa H. “Noninvasive heart rate monitoring system for avian embryos based on the ballistocardiogram.” Medical & Biological Engineering & Computing, vol.27, pp.399-404, 1989.
. Lewin R, Dörner M and Tönhardt H. “Pulse oximetry: a new way of determining the heart rate in chicken embryos.” Pflügers Archiv, vol.434, pp.639-641,1997.
. Mcquinn T C, Bratoeva M, Dealmeida A, Remond M, Thompson R P and Sedmera D. “High-frequency ultrasonographic imaging of avian cardiovascular development.” Developmental Dynamics, vol.236, pp.3503-3513,2007.
. Schellpfeffer M A and Kolesari G L. “Microbubble contrast imaging of the cardiovascular system of the chick embyro.” Ultrasound in Medicine & Biology, vol.38, pp.504-510,2012.
. Smith D P, Mauldin J M, Lawrence K C, Park B and Heitschmidt g w. “Detection of fertility and early development of hatching eggs with hyperspectral imaging.” XIth European Symposium on the Quality of Eggs and Egg Products, 2005, pp.176-180.
. Jones S T, Shattuck R E and Center A I. “Detection of Early Embryonic Development in hatehing eggs: a hyperspcctral imaging systems and neural network approach.” Johns Hopkins Apl Thchnical Digest, 2005, pp.67-73.
. Smith D P, Lawrence K C and Heitschmidt G W. “Fertility and embryo development of broiler hatching eggs evaluated with a hyperspectral imaging and predictive modeling system.” International journal of poultry science, vol.7, pp.1001-5101004, 2008.
. Liu L and Ngadi M O. “Detecting Fertility and Early Embryo Development of Chicken Eggs Using Near-Infrared Hyperspectral Imaging.” Food and Bioprocess Technology, vol.6, pp.2503-2513,2013.
. Zhu Z, Liu T and Ma M. “Hatching eggs nondestructive detection based on hyperspectral-imaging information and RVM.” Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering, vol.31, pp.285-292, 2015.
. Das K and Evans M D. “Detecting Fertility of Hatching Eggs Using Machine Vision I. Histogram Characterization Method.” Transactions of the Asae, vol.35, pp.1335-1341, 1992.
. Das K and Evans M D. “Detecting Fertility of Hatching Eggs Using Machine Vision II: Neural Network Classifiers.” Transactions of the Asae, vol.35, pp.2015-2041, 1992.
. Xu Q L and Cui F Y. “Non-destructive detection on the fertility of injected SPF eggs in vaccine manufacture.” Proceedings of the 26th Chinese Control and Decision Conference, Changsha, PEOPLES R CHINA, 2014, pp.1574-1579.
. Shan B. “Fertility Detection of Middle-stage Hatching Egg in Vaccine Production Using Machine Vision.” Proceedings of the Second International Workshop on Education Technology and Computer Science, Wuhan, China, 2010, pp.95-98.
. He K, Zhang X, Ren S and Sun J. “Deep residual learning for image recognition.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2016, pp.770-778.
. Hu J, Shen L and Sun G. “Squeeze-and-Excitation Networks.” 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2018.
. Long J, Shelhamer E and Darrell T. “Fully convolutional networks for semantic segmentation.” Proceedings of the 2015 Conference on Computer Vision and Pattern Recognition, Boston, MA, 2015, pp.3431-3440.
. Kendall A, Cipolla R and Badrinarayanan V. “Segnet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation.” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.39, pp.2481-2495, 2017.
. Chen L C, Papandreou G, Kokkinos I and Murphy K, Yuille A L. Semantic image segmentation with deep convolutional nets and fully connected crfs[J]. arXiv preprint arXiv:1412.7062, 2014
. Geng L, Qiu L, Wu J and Xiao Z. “Blood vessels segmentation of hatching eggs based on fully convolutional networks.” Ninth International Conference on Graphic and Image Processing, 2018.
Downloads
Published
How to Cite
Issue
Section
License
Authors who submit papers with this journal agree to the following terms.
