Infrared Spectroscopic Image Segmentation Based on Neural Immune Network With Growing Immune Field
ZHOU Zi-jie1, ZHANG Bao-feng2*, YU Xiao2*
1. School of Computer Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
2. School of Electrical and Electronic Engineering, Tianjin University of Technology, Tianjin 300384, China
Abstract：Pipeline transportation has great advantages in long-distance transportation of oil and gas, and it is crucial to detect pipeline safety. In order to ensure the effective detection of the pipeline condition at any time, infrared imaging technology is of great significance in the field of pipeline detection because it can reflect the characteristics of the target according to the thermal radiation information of the object and ignore the influence of visible light.However, due to the diversity of the outdoor environment, infrared pipeline images have many problems, such as the uneven distribution of target features, pipeline occlusion and background target interference. These problems increase the difficulty of extracting pipeline targets, which is not conducive to the segmentation and detection of pipelines. The biological immune system exhibits excellent recognition, learning, memory, tolerance and coordination in antigen detection, extraction and elimination. These characteristics are lacking in current complex system optimization strategies. Based on the biological nervous system’s mechanism regulating immune system, a neural immune network is designed to detect and extract infrared pipeline targets in complex background. According to the regulatory mechanism of the biological neural network in the immune system, the neural network for infrared pipeline target location is constructed using the basic pipeline shape feature model. In this paper, three typical infrared pipeline images are selected, and the traditional target detection algorithm is compared with the algorithm based on neural immune network. The true positive rate of the traditional target detection algorithm is 0.405 6, and the neural immune network algorithm is 0.980 5. The Jaccard similarity coefficient of the traditional target detection algorithm is 0.271 8, and the neural immune network algorithm is 0.944 4. The absolute error rate is 0.117 5, and the neural immune network algorithm is 0.011 8. The results show that the true positive rate of the neural immune network algorithm is 0.574 9 higher than that of the traditional algorithm, and the absolute error rate is 0.105 7 lower. It proves that the proposed algorithm can extract the complete infrared pipeline target more accurately than the traditional method in the complex background. This network structure can improve the detection efficiency for pipeline safety.
[1] Yang N, Yuan M, Wang P, et al. Journal of the Science of Food & Agriculture, 2019.
[2] Zhang J, Wang Y. Semiconductor Optoelectronics, 2016, 37(1): 126.
[3] Prakash R M, Bhuvaneshwari K, Divya M, et al. 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), 2017, 1.
[4] Bai X, Wang Y, Liu H, et al. IEEE Transactions on Fuzzy Systems, 2018, 26(4):1946.
[5] Li L, Zhou F, Bai X. IEEE Transactions on Intelligent Transportation Systems, 2017, 99: 1.
[6] Fuliang H, Yongcai G, Chao G. Optics & Laser Technology, 2019, 110: 114.
[7] Wei L, Yi-Wei C, Microbiology D O. Progress in Biochemistry & Biophysics, 2017, 44(12): 1066.
[8] Fu D, Sun J. Proceedings of 2017 9th International Conference On Modelling, Identification and Control, 2017, 1026.
[9] Yu X, Zhou Z, Gao Q, et al. Infrared Physics & Technology, 2018, 88: 184.
[10] Yu Xiao, Zhou Zijie, Kamil Riha. Spectroscopy and Spectral Analysis, 2018, 38(11): 3645.
[11] Chavan S S, Pavlov V A, Tracey K J. Immunity, 2017, 46(6): 927.
[12] Ribeiro-Da-Silva M, Vasconcelos D M, Alencastre I S, et al. Scientific Reports, 2018, 8(1).
[13] Pavlov V A, Tracey K J. Nature Neuroscience, 2017, 20(2): 156.
[14] Sébastien Talbot, Foster S L, Woolf C J. Annual Review of Immunology, 2016, 34(1): 421.
[15] Ayesha O, Anam N, Aqsa I, et al. Scientific Reports, 2018, 8(1): 8874.
