Abstract
The plumpness of the Pacific oyster gonad, the reproductive organs of both male and female oysters which are buried within the flesh of the oyster in the shell, has important implications for the quality and breeding of subsequent parents. At present, only the conventional method of breaking their shells allows for the observation and study of the interior tissues of Pacific oysters. In this paper, the gonad of Pacific oyster was observed by small animal Magnetic Resonance Imaging (MRI), and a multi-effective feature fusion network algorithm R-SINet was proposed for the detection of unapparent target, in Nuclear Magnetic Resonance (NMR) images, which can effectively solve the problem that the gonads of Pacific oysters are difficult to identify from the background images. In addition, the gray histogram of the segmented gonad region was calculated, and it was found that the female and male had differences in gray value. The sex of oyster was nondestructively detecting by this task. Firstly, established the Oyster gonad datasets; secondly, a compact pyramid refinement module that combines with high-level semantic features and low-level semantic features was proposed, designed a lightweight decoder to improve the accuracy of feature fusion; thirdly, a switchable excitation model capable of adaptive recalibration is proposed to obtain an attention map. Experimental results on the Oyster gonad datasets demonstrate the effectiveness of the method. Comparing R-SINet’s experimental findings to those of popular algorithm models, such as the benchmark algorithm SINet_v2, revealed promising results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Yang, L.: Introduction to the management of hospital Bruker BioSpec94/30 USR type small animal MRI research equipment. China Equip. Eng. 51–52 (2022)
Zhang, Z.-N., Zheng, Y., Wang, X.-M.: Application of 7.0T small animal MRI to study the progress of Alzheimer’s disease. China Med. Imaging Technol. 930–933 (2019)
Hang, K.-B., Su, W.-W., Huang, J., Bao, G.-J., Liu, W.-H., Li, S.-P.: 7.0T small animal MR instrumentation to observe brain injury in a rat model of classic pyrexia. China Med. Imaging Technol. 38, 481–485 (2022)
Webster, B.: Handbook of small animal MRI. Aust. Veterinary J. 88, 407 (2010)
Gilchrist, S., et al.: A simple, open and extensible gating control unit for cardiac and respiratory synchronisation control in small animal MRI and demonstration of its robust performance in steady-state maintained CINE-MRI. Magn. Reson. Imaging 81, 1–9 (2021)
Liu, W.-L., et al.: Enhanced medial prefrontal cortex and hippocampal activity improves memory generalization in APP/PS1 Mice: a multimodal animal MRI study. Front. Cell. Neurosci. 16, 848967 (2022)
Fan, D.-P., Ji, G.-P., Sun, G.-L., Cheng, M.-M., Shen, J.-B., Shao, L.: Camouflaged object detection. In: CVPR, pp. 2774–2784 (2020)
Lv, Y.-Q., et al.: Simultaneously localize, segment and rank the camouflaged objects. In: CVPR, pp. 11591–11601 (2021)
Chen, S., Tan, X., Wang, B., Hu, X.: Reverse attention for salient object detection. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11213, pp. 236–252. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01240-3_15
Zhai, Q., Li, X., Yang, F., Chen, C.-L.-Z., Cheng, H., Fan, D.-P.: Mutual graph learning for camouflaged object detection. In: CVPR, pp. 12997–13007 (2021)
Fan, D.-P., Ji, G.-P., Cheng, M.-M., Shao, L.: Concealed object detection. IEEE Trans. Pattern Anal. Mach. Intell. 44, 6024–6042 (2021)
Jia, Q., Yao, S.-L., Liu, Y., Fan, X., Liu, R.-S., Luo, Z.-X.: Segment, magnify and reiterate: detecting camouflaged objects the hard way. In: CVPR, pp. 4713–4722 (2022)
Fu, K., Fan, D.-P., Ji, G.-P., Zhao, Q.-J., Shen, J.-B., Zhu C.: Siamese network for RGB-D salient object detection and beyond. IEEE Trans. Pattern Anal. Mach. Intell. 1 (2021)
Li, Chongyi, Cong, Runmin, Piao, Yongri, Xu, Qianqian, Loy, Chen Change: RGB-D salient object detection with cross-modality modulation and selection. In: Vedaldi, Andrea, Bischof, Horst, Brox, Thomas, Frahm, Jan-Michael. (eds.) ECCV 2020. LNCS, vol. 12353, pp. 225–241. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58598-3_14
Gao, S.-H., Cheng, M.-M., Zhao, K., Zhang, X.-Y., Yang, M.-H., Torr, P.: Res2Net: a new multi-scale backbone architecture. IEEE Trans. Pattern Anal. Mach. Intell. 43, 652–662 (2019)
Howard, A.-G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)
Li, J.-H., Li, B., Xu, J.-Z., Xiong, R.-Q., Gao, W.: Fully connected network-based intra prediction for image coding. IEEE Trans. Image Process. 27, 3236–3247 (2018)
Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B.: Recent advances in convolutional neural networks. Pattern Recogn. 77, 354–377 (2018)
Liang, S., Huang, Z., Liang, M., Yang, H.: Instance enhancement batch normalization: an adaptive regulator of batch noise. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 4819–4827 (2020)
Kingma, D.-P., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representation (2015)
Fan, D.-P., Cheng, M.-M., Liu, Y., Li, T., Borji, A.: Structure-measure: a new way to evaluate foreground maps. In: ICCV, pp. 4548–4557 (2017)
Fan, D.-P., Gong, C., Cao, Y., Ren, B., Cheng, M.-M., Borji, A.: Enhanced alignment measure for binary foreground map evaluation. In: IJCAI (2018)
Ran, M., Lihi, Z.-M., Ayellet, T.: How to evaluate foreground maps? In: IEEE CVPR, pp. 248–255 (2014)
Perazzi, F., Krähenbühl, P., Pritch, Y., Hornung, A.: Saliency filters: contrast based filtering for salient region detection. In: CVPR, pp. 733–740 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Chen, Y., Yue, J., Li, Z., Yang, J., Wang, W. (2024). Pacific Oyster Gonad Identification and Grayscale Calculation Based on Unapparent Object Detection. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14436. Springer, Singapore. https://doi.org/10.1007/978-981-99-8555-5_8
Download citation
DOI: https://doi.org/10.1007/978-981-99-8555-5_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8554-8
Online ISBN: 978-981-99-8555-5
eBook Packages: Computer ScienceComputer Science (R0)