In this paper, an improved Fast-R-CNN nuclear power cold source disaster biological image recognition algorithm is proposed to improve the safety operation of nuclear power plants. Firstly, the image data sets of the disaster-causing creatures hairy shrimp and jellyfish were established. Then, in order to solve the problems of low recognition accuracy and unrecognizable small entities in disaster biometrics, Gamma correction algorithm was used to optimize the image of the data set, improve the image quality and reduce the noise interference. Transposed convolution is introduced into the convolution layer to increase the recognition accuracy of small targets. The experimental results show that the recognition rate of this algorithm is 6.75%, 7.5%, 9.8% and 9.03% higher than that of ResNet-50, MobileNetv1, GoogleNet and VGG16, respectively. The actual test results show that the accuracy of this algorithm is obviously better than other algorithms, and the recognition efficiency is higher, which basically meets the preset requirements of this paper.
References
[1]
Zeng, L., Chen, G., Wang, T., et al. (2021) Acoustic Study on the Outbreak of Creseise acicula nearby the Daya Bay Nuclear Power Plant Base during the Summer of 2020. Marine Pollution Bulletin, 165, Article ID: 112144. https://doi.org/10.1016/j.marpolbul.2021.112144
[2]
Kang, K., Ouyang, W., Li, H., et al. (2016) Object Detection from Video Tubelets with Convolutional Neural Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, 27-30 June 2016, 817-825. https://doi.org/10.1109/CVPR.2016.95
[3]
Khelifi, L. and Mignotte, M. (2020) Deep Learning for Change Detection in Remote Sensing Images: Comprehensive Review and Meta-Analysis. IEEE Access, 8, 126385-126400. https://doi.org/10.1109/ACCESS.2020.3008036
[4]
Doehring, K., Young, R.G., Hay, J., et al. (2011) Suitability of Dual-Frequency Identification Sonar (DIDSON) to Monitor Juvenile Fish Movement at Floodgates. New Zealand Journal of Marine and Freshwater Research, 45, 413-422. https://doi.org/10.1080/00288330.2011.571701
[5]
Olivieri, M., Glegg, S.A.L. and Coulson, R.K. (1998) Measurements of Snapping Shrimp Colonies Using a Wideband Mobile Passive Sonar. The Journal of the Acoustical Society of America, 103, 41-47. https://doi.org/10.1121/1.421732
[6]
Zhang, J., Wu, Z. and An, C. (2021) Research on the Detection and Early Warning Technology of Harmful Marine Organisms in the Water Intake of Nuclear Power Plant by 3D Image Sonar. E3S Web of Conferences, 290, Article No. 03013. https://doi.org/10.1051/e3sconf/202129003013
[7]
Yu, X., Tang, L., Wu, X., et al. (2018) Nondestructive Freshness Discriminating of Shrimp Using Visible/Near-Infrared Hyperspectral Imaging Technique and Deep Learning Algorithm. Food Analytical Methods, 11, 768-780. https://doi.org/10.1007/s12161-017-1050-8
[8]
Brewin, R.J.W., Hardman-Mountford, N., Lavender, S.J., et al. (2011) An Intercomparison of Bio-Optical Techniques for Detecting Dominant Phytoplankton Size Class from Satellite Remote Sensing. Remote Sensing of Environment, 115, 325-339. https://doi.org/10.1016/j.rse.2010.09.004
[9]
Wang, M. (2007) Remote Sensing of the Ocean Contributions from Ultraviolet to Near-Infrared Using the Shortwave Infrared Bands: Simulations. Applied Optics, 46, 1535-1547. https://doi.org/10.1364/AO.46.001535
[10]
Kay, S., Hedley, J.D. and Lavender, S. (2009) Sun Glint Correction of High and Low Spatial Resolution Images of Aquatic Scenes: A Review of Methods for Visible and Near-Infrared Wavelengths. Remote Sensing, 1, 697-730. https://doi.org/10.3390/rs1040697
[11]
Des Marais, D.J., Harwit, M.O., Jucks, K.W., et al. (2002) Remote Sensing of Planetary Properties and Biosignatures on Extrasolar Terrestrial Planets. Astrobiology, 2, 153-181. https://doi.org/10.1089/15311070260192246
[12]
Kavanaugh, M.T., Bell, T., Catlett, D., et al. (2021) Satellite Remote Sensing and the Marine Biodiversity Observation Network. Oceanography, 34, 62-79. https://doi.org/10.5670/oceanog.2021.215
[13]
Basu, B., Sannigrahi, S., Sarkar Basu, A., et al. (2021) Development of Novel Classification Algorithms for Detection of Floating Plastic Debris in Coastal Waterbodies Using Multispectral Sentinel-2 Remote Sensing Imagery. Remote Sensing, 13, Article No. 1598. https://doi.org/10.3390/rs13081598
[14]
Shortis, M.R. and Harvey, E.S. (1998) Design and Calibration of an Underwater Stereo-Video System for the Monitoring of Marine Fauna Populations. International Archives of Photogrammetry and Remote Sensing, 32, 792-799.
[15]
Demir, H.S., Christen, J.B. and Ozev, S. (2020) Energy-Efficient Image Recognition System for Marine Life. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 39, 3458-3466. https://doi.org/10.1109/TCAD.2020.3012745
[16]
Harvey, E.S. and Shortis, M.R. (1998) Calibration Stability of an Underwater Stereo-Video System: Implications for Measurement Accuracy and Precision. Marine Technology Society Journal, 32, 3-17.
[17]
Zhao, X.Q., Peng, H.M. and Gao, Q. (2017) A Video Image Detection Method for Fish Body Motion Characteristics. Shaanxi: Journal of Xi’an University of Posts and Telecommunications, 22, 38-43.
[18]
Lai, Y. (2019) A Comparison of Traditional Machine Learning and Deep Learning in Image Recognition. Journal of Physics: Conference Series, 1314, Article ID: 012148. https://doi.org/10.1088/1742-6596/1314/1/012148
[19]
Sharifara, A., Rahim, M.S.M. and Anisi, Y. (2014) A General Review of Human Face Detection Including a Study of Neural Networks and Haar Feature-Based Cascade Classifier in Face Detection. 2014 IEEE International Symposium on Biometrics and Security Technologies (ISBAST), Kuala Lumpur, 26-27 August 2014, 73-78. https://doi.org/10.1109/ISBAST.2014.7013097
[20]
Gao, H., Dou, L., Chen, W., et al. (2013) Image Classification with Bag-of-Words Model Based on Improved Sift Algorithm. 2013 IEEE 9th Asian Control Conference (ASCC), Istanbul, 23-26 June 2013, 1-6. https://doi.org/10.1109/ASCC.2013.6606268
[21]
Li, W.S. and Peng, D. (2013) Object Recognition Based on the Region of Interest and Optical Bag of Words Model. Proceedings of the 5th International Conference on Internet Multimedia Computing and Service, Huangshan, 17-19 August 2013, 394-398. https://doi.org/10.1145/2499788.2499873
[22]
LeCun, Y., Bengio, Y. and Hinton, G. (2015) Deep Learning. Nature, 521, 436-444. https://doi.org/10.1038/nature14539
[23]
Deng, L. and Yu, D. (2014) Deep Learning: Methods and Applications. Foundations and Trends® in Signal Processing, 7, 197-387. https://doi.org/10.1561/2000000039
[24]
Heaton, J.T. (2018) Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Deep Learning: The MIT Press, 2016, 800 pp, ISBN: 0262035618. Genetic Programming and Evolvable Machines, 19, 305-307. https://doi.org/10.1007/s10710-017-9314-z
[25]
Wu, M. and Chen, L. (2015) Image Recognition Based on Deep Learning. 2015 IEEE Chinese Automation Congress (CAC), Wuhan, 27-29 November 2015, 542-546.
[26]
Fang, W., Zhang, F., Sheng, V.S., et al. (2018) A Method for Improving CNN-Based Image Recognition Using DCGAN. Computers, Materials & Continua, 57, 167-178. https://doi.org/10.32604/cmc.2018.02356
[27]
Hijazi, S., Kumar, R. and Rowen, C. (2015) Using Convolutional Neural Networks for Image Recognition. Cadence Design Systems Inc., San Jose.
[28]
Chauhan, R., Ghanshala, K.K. and Joshi, R.C. (2018) Convolutional Neural Network (CNN) for Image Detection and Recognition. 2018 IEEE 1st International Conference on Secure Cyber Computing and Communication (ICSCCC), Jalandhar, 15-17 December 2018, 278-282. https://doi.org/10.1109/ICSCCC.2018.8703316
[29]
Xiong, J., Yu, D., Liu, S., et al. (2021) A Review of Plant Phenotypic Image Recognition Technology Based on Deep Learning. Electronics, 10, 81. https://doi.org/10.3390/electronics10010081
[30]
Dong, Y. and Liang, G. (2019) Research and Discussion on Image Recognition and Classification Algorithm Based on Deep Learning. 2019 IEEE International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), Taiyuan, 8-10 November 2019, 274-278. https://doi.org/10.1109/MLBDBI48998.2019.00061
[31]
Boddapati, V., Petef, A., Rasmusson, J., et al. (2017) Classifying Environmental Sounds Using Image Recognition Networks. Procedia Computer Science, 112, 2048-2056. https://doi.org/10.1016/j.procs.2017.08.250
[32]
Kim, J.H., Kim, N., Park, Y.W., et al. (2022) Object Detection and Classification Based on YOLO-V5 with Improved Maritime Dataset. Journal of Marine Science and Engineering, 10, 377. https://doi.org/10.3390/jmse10030377
[33]
Singnoo, J. and Finlayson, G.D. (2010) Understanding the Gamma Adjustment of Images. 18th Color and Imaging Conference, San Antonio, 8-12 November 2010, 134-139. https://doi.org/10.2352/CIC.2010.18.1.art00024
[34]
Zhang, X. and Zhang, C. (2007) Satellite Cloud Image De-Noising and Enhancement by Fuzzy Wavelet Neural Network and Genetic Algorithm in Curvelet Domain. In: International Conference on Life System Modeling and Simulation, Springer, Berlin, 389-395. https://doi.org/10.1007/978-3-540-74769-7_42
[35]
Guo, R., Dai, Q. and Hoiem, D. (2012) Paired Regions for Shadow Detection and Removal. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35, 2956-2967. https://doi.org/10.1109/TPAMI.2012.214
