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Active phase recognition method of hydrogenation catalyst based on multi-feature fusion Mask CenterNet

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Abstract

In order to realize the intelligent recognition and statistics of hydrogenation catalyst image information, this paper presents a new method to judge the active phase by image recognition, which is different from traditional methods. Firstly, considering that hydrogenation catalyst image targets are small and easy to stack, the feature extraction network in the CenterNet model is optimized by adding the multi-feature fusion module to improve the accuracy of the network in edge positioning. Secondly, according to the linear shape of the hydrogenation catalyst, the mask branch is added to the CenterNet model to train the hydrogenation catalyst stripes with unclear target to reduce the leakage rate of the hydrogenation catalyst. The experimental results show that the detection accuracy of the improved CenterNet network is 91\(\%\), 7\(\%\) higher than that of the original one, with a decline in detection rate by 12\(\%\). The method proposed in this paper can accurately identify and segment the hydrogenation catalyst in the electron microscope image, which can provide technical support for the statistics and analysis of the hydrogenation catalyst image.

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References

  1. Wang S et al (2018) Structure-activity relationship of supported au catalysts with high catalytic activity by modifying the inactive supports. Surf Interface Anal 50(9):843–850. https://doi.org/10.1002/sia.6478

    Article  Google Scholar 

  2. Liu H et al (2017) Pvp-assisted synthesis of unsupported nimo catalysts with enhanced hydrodesulfurization activity. Fuel Process Technol 160:93–101. https://doi.org/10.1016/j.fuproc.2017.02.018

    Article  Google Scholar 

  3. Shipitcyna A et al (2016) Characterization and activity of Pd–Ir catalysts in Co and C3h6 oxidation under stoichiometric conditions. Top Catal 59(13–14):1097–1103. https://doi.org/10.1007/s11244-016-0628-5

    Article  Google Scholar 

  4. Lokhande S et al (2015) High catalytic activity of Pt-Pd containing Usy Zeolite catalyst for low temperature co oxidation from industrial off gases. Atmos Pollut Res 6(4):589–595. https://doi.org/10.5094/apr.2015.066

    Article  Google Scholar 

  5. Dat NM et al (2020) Synthesis of silver/reduced graphene oxide for antibacterial activity and catalytic reduction of organic dyes. Synth Metals. https://doi.org/10.1016/j.synthmet.2019.116260

    Article  Google Scholar 

  6. Xu Y et al (2012) Catalyst activity enhancement of PtRu/CB for methanol oxidation by carbon nanotube doping. IEEE Trans Nanotechnol 11(1):148–151. https://doi.org/10.1109/TNANO.2011.2162249

    Article  Google Scholar 

  7. Fraz MM et al (2020) Fabnet: feature attention-based network for simultaneous segmentation of microvessels and nerves in routine histology images of oral cancer. Neural Comput Appl 32(14):9915–9928. https://doi.org/10.1007/s00521-019-04516-y

    Article  Google Scholar 

  8. Gite S et al (2022) Enhanced lung image segmentation using deep learning. Neural Comput Appl. https://doi.org/10.1007/s00521-021-06719-8

    Article  Google Scholar 

  9. Hao Li-Ying et al (2023) Trca-Net: stronger U structured network for human image segmentation. Neural Comput Appl 35(13):9627–9635. https://doi.org/10.1007/s00521-023-08199-4

    Article  Google Scholar 

  10. Kaur Amrita et al (2021) Ga-Unet: Unet-based framework for segmentation of 2d and 3d medical images applicable on heterogeneous datasets. Neural Comput Appl 33(21):14991–15025. https://doi.org/10.1007/s00521-021-06134-z

    Article  Google Scholar 

  11. Sun G et al (2021) Joint optic disc and cup segmentation based on multi-scale feature analysis and attention pyramid architecture for glaucoma screening. Neural Comput Appl. https://doi.org/10.1007/s00521-021-06554-x

    Article  Google Scholar 

  12. Zhu Zijiang et al (2021) Indoor scene segmentation algorithm based on full convolutional neural network. Neural Comput Appl 33(14):8261–8273. https://doi.org/10.1007/s00521-020-04961-0

    Article  Google Scholar 

  13. Pattichis MS et al (2000) AM–FM texture segmentation in electron microscopic muscle imaging. IEEE Trans Med Imaging 19(12):1253–1257. https://doi.org/10.1109/42.897818

    Article  Google Scholar 

  14. Agarwal S, et al. (2023) Comparing U-Net and mask R-CNN algorithms for deep learning-based segmentation of electron microscopy images containing cavities for nuclear reactor applications. In: 2023 3rd International conference on electrical, computer, communications and mechatronics engineering (ICECCME), pp.148-151, https://doi.org/10.1109/ICECCME57830.2023.10252280

  15. Zhang J et al (2015) Context-based segmentation of renal corpuscle from microscope renal biopsy image sequence. IEICE Trans Fundament Electron Commun Comput Sci E98.A:1114–1121. https://doi.org/10.1587/transfun.E98.A.1114

    Article  Google Scholar 

  16. Bell CG et al (2022) Trainable segmentation for transmission electron microscope images of inorganic nanoparticles. J Microsc 288:169–184. https://doi.org/10.1111/jmi.13110

    Article  Google Scholar 

  17. You ZZ et al (2022) Multiscale segmentation- and error-guided iterative convolutional neural network for cerebral neuron segmentation in microscopic images. Microsc Res Tech 85:3541–3552. https://doi.org/10.1002/jemt.24206

    Article  Google Scholar 

  18. Liu XB et al (2020) Deep learning for cardiac image segmentation: a review. Front Cardiovasc Med. https://doi.org/10.3389/fcvm.2020.00025

    Article  Google Scholar 

  19. Al-Kofahi Y et al (2018) A deep learning-based algorithm for 2-D cell segmentation in microscopy images. BMC Bioinformatics. https://doi.org/10.1186/s12859-018-2375-z

    Article  Google Scholar 

  20. Xu Y et al (2018) Deep learning approaches in electron microscopy imaging for mitochondria segmentation. Int J Data Min Bioinform 21(2):91–106. https://doi.org/10.1109/TNANO.2011.2162249

    Article  Google Scholar 

  21. Nikishin I et al (2021) Scanev-a neural network-based tool for the automated detection of extracellular vesicles in TEM images. Micron. https://doi.org/10.1016/j.micron.2021.103044

    Article  Google Scholar 

  22. Oktay AB, Gurses A (2019) Automatic detection, localization and segmentation of nano-particles with deep learning in microscopy images. Micron 120:113–119. https://doi.org/10.1016/j.micron.2019.02.009

    Article  Google Scholar 

  23. Rey JS et al (2021) Deep-learning in situ classification of Hiv-1 virion morphology. Comput Struct Biotechnol J 19:5688–5700. https://doi.org/10.1016/j.csbj.2021.10.001

    Article  Google Scholar 

  24. Aboy-Pardal MCM et al (2023) A deep learning-based tool for the automated detection and analysis of caveolae in transmission electron microscopy images. Comput Struct Biotechnol J 21:224–237. https://doi.org/10.1016/j.csbj.2022.11.0622001-0370

    Article  Google Scholar 

  25. Sun ZJ et al (2022) A deep learning-based framework for automatic analysis of the nanoparticle morphology in SEM/TEM images. Nanoscale 14(30):10761–10772. https://doi.org/10.1039/d2nr01029a

    Article  Google Scholar 

  26. Li HG et al (2018) Superpixel-based feature for aerial image scene recognition. Sensors. https://doi.org/10.3390/s18010156

    Article  Google Scholar 

  27. Tian ZQ et al (2016) Superpixel-based segmentation for 3d prostate MR images. IEEE Trans Med Imaging 35(3):791–801. https://doi.org/10.1109/tmi.2015.2496296

    Article  MathSciNet  Google Scholar 

  28. Yang QH et al (2020) Superpixel-based segmentation algorithm for mature citrus. Int J Agric Biol Eng 13(4):166–171. https://doi.org/10.25165/j.ijabe.20201304.5607

    Article  MathSciNet  Google Scholar 

  29. Liu YP et al (2022) Layer segmentation of oct fingerprints with an adaptive gaussian prior guided transformer. IEEE Trans Instrum Measurement. https://doi.org/10.1109/tim.2022.3212113

    Article  Google Scholar 

  30. Sinduja A, Suruliandi A (2018) Block-based tri-channel hybrid segmentation of images for foreground extraction. Sadhana-Acad Proceed Eng Sci. https://doi.org/10.1007/s12046-018-0955-2

    Article  Google Scholar 

  31. Sheng H, et al. (2022) Foreign fibers detection using improved otsu-based maximum entropy algorithm in spinning process. In: 2022 6th International conference on robotics and automation sciences (ICRAS), pp. 206-210, https://doi.org/10.1109/ICRAS55217.2022.9842190

  32. Zhang H, Zhang HJ (2020) A novel segmentation method for brain MRI using a block-based integrated fuzzy C-means clustering algorithm. J Med Imaging Health Inform 10(3):579–585. https://doi.org/10.1166/jmihi.2020.2970

    Article  Google Scholar 

  33. Kumar A et al (2020) Semi-supervised Otsu based hyperbolic tangent gaussian kernel fuzzy C-mean clustering for dental radiographs segmentation. Multimed Tools Appl 79(3–4):2745–2768. https://doi.org/10.1007/s11042-019-08268-8

    Article  Google Scholar 

  34. Malik YS et al (2022) Applying an adaptive Otsu-based initialization algorithm to optimize active contour models for skin lesion segmentation. J Xray Sci Technol 30(6):1169–1184. https://doi.org/10.3233/xst-221245

    Article  Google Scholar 

  35. Noval JJS, Gómez‐Merchán R, Leñero‐Bardallo JA, Gontard LC (2023) TEMAS: a flexible non‐ai algorithm for metrology of single‐core and core‐shell nanoparticles from TEM images. Part Part Syst Charact 40(2):2200170. https://doi.org/10.1002/ppsc.202200170

    Article  Google Scholar 

  36. He KM, et al. (2018) Mask R-CNN. arXiv:1703.06870v3 [cs.CV] 24 Jan

  37. Zhang F et al (2021) Rodlike nanoparticle parameter measurement method based on improved Mask R-CNN segmentation. Signal Image Video Process 15:579–587. https://doi.org/10.1007/s11760-020-01779-0

    Article  Google Scholar 

  38. Loh D et al (2021) A deep learning approach to the screening of malaria infection: automated and rapid cell counting, object detection and instance segmentation using Mask R-CNN. IEEE Trans Nanotechnol. https://doi.org/10.1016/j.compmedimag.2020.101845

    Article  Google Scholar 

  39. Zhou X et al. (2019) Objects as Points. arXiv:1904.07850v2 [cs.CV] 25 Apr

  40. Lon J et al. (2015) Fully convolutional networks for semantic segmentation. arXiv:1411.4038v2 [cs.CV] 8 Mar

  41. Al-Ameen Z (2021) Contrast enhancement of digital images using an improved type-II fuzzy set-based algorithm. Traitement Du Signal 38(1):39–50. https://doi.org/10.18280/ts.380104

    Article  Google Scholar 

  42. Jebadass JR, Balasubramaniam P (2022) Low contrast enhancement technique for color images using interval-valued intuitionistic fuzzy sets with contrast limited adaptive histogram equalization. Soft Comput 26(10):4949–4960. https://doi.org/10.1007/s00500-021-06539-x

    Article  Google Scholar 

  43. Liu S et al. (2018) Path aggregation network for instance segmentation. arXiv:1803.01534v4 [cs.CV] 18 Sep

  44. Liu S et al. (2019) Learning spatial fusion for single-shot object detection. arXiv:1911.09516v2 [cs.CV] 25 Nov

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Authors and Affiliations

  1. School of Automation, Shenyang Aerospace University, Daoyi South Street, Shenyang, 110136, Shenbei New Area, China

    Zhujun Wang, Haobin Li, Ailin Cui & Song Bao

  2. School of Electrical Engineering, Shenyang Institute of Engineering Street, Puchang Road, Shenyang, 110136, Shenbei New Area, China

    Tianhe Sun

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Wang, Z., Sun, T., Li, H. et al. Active phase recognition method of hydrogenation catalyst based on multi-feature fusion Mask CenterNet. Neural Comput & Applic 36, 8711–8725 (2024). https://doi.org/10.1007/s00521-024-09544-x

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