1School of Mechanical Engineering, Zhengzhou University of Science and Technology, Zhengzhou, 450015 China
Received: March 7, 2022 Accepted: April 17, 2022 Publication Date: May 13, 2022
Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.
In the maintenance of outdoor power equipment, the enhancement and segmentation of infrared heat map is the key to the intelligent development of diagnosis and troubleshooting in the future. This paper presents a new model for enhancement and segmentation of infrared heat map of power equipment in complex environment. Retinex image enhancement model is improved by region joint prior information constraint and gamma transform. In this paper, multi-scale structure reserved smoothing filter is proposed, and the filter size is constrained by phase stretch transformation. The new model can not only estimate and compensate the hidden noise, increase the contrast of the infrared heat map, but also eliminate the filtering edge dispersion phenomenon, which is suitable for power equipment segmentation of various sizes. Experimental results show that compared with other algorithms, the new model can obtain more complete and high contrast infrared heat map in complex environment, and has the performance of removing most background interference.
 H. Ren, X. Ye, J. Nie, J. Meng, W. Fan, Q. Qin, Y. Liang, and H. Liu, (2021) “Retrieval of land surface temperature, emissivity, and atmospheric parameters from hyperspectral thermal infrared image using a feature-band linear-format hybrid algorithm" IEEE Transactions on Geoscience and Remote Sensing 60: 1–15. DOI: 10.1109/TGRS.2020.3047381.
 Z. Zhang, H. Wang, T. Liu, Y. Wang, H. Zhang, F. Yuan, X. Yang, S. Xu, and Y. Meng, (2021) “Accurate detection method of pig’s temperature based on non-point source thermal infrared image" CAAI Transactions on Intelligence Technology 6(3): 312–323. DOI: 10.1049/cit2.12017.
 D. Müller, A. Ehlen, and B. Valeske, (2021) “Convolutional neural networks for semantic segmentation as a tool for multiclass face analysis in thermal infrared" Journal of nondestructive evaluation 40(1): 1–10. DOI: 10.1007/s10921-020-00740-y.
 M. Younsi, M. Diaf, and P. Siarry, (2020) “Automatic multiple moving humans detection and tracking in image sequences taken from a stationary thermal infrared camera" Expert Systems with Applications 146: 113171. DOI: 10.1016/j.eswa.2019.113171.
 P. Zhuang and X. Ding, (2020) “Underwater image enhancement using an edge-preserving filtering Retinex algorithm" Multimedia Tools and Applications 79(25): 17257–17277. DOI: 10.1007/s11042-020-08739-3.
 H. Asmuni, R. M. Othman, R. Hassan, et al., (2013) “An improved multiscale retinex algorithm for motionblurred iris images to minimize the intra-individual variations" Pattern Recognition Letters 34(9): 1071–1077. DOI: 10.1016/j.patrec.2013.02.017.
 S. Yin, Y. Zhang, and S. Karim, (2018) “Large scale remote sensing image segmentation based on fuzzy region competition and Gaussian mixture model" IEEE Access 6: 26069–26080. DOI: 10.1109/ACCESS.2018.2834960.
 Y. Li, G. Blois, F. Kazemifar, and K. Christensen. “A Particle-Based Image Segmentation Method for Phase Separation and Interface Detection in PIV Images of Multiphase Flow in Porous Media”. In: 2021. DOI: 10.1088/1361-6501/abf0dc.
 H. Zou and F. Huang, (2016) “Infrared image segmentation for electrical equipment based on fast-match algorithm" Infrared Technology 38(1): 21–27.
 Q. Wang, J. Xue, and X. Ren, (2016) “An Adaptive Segmentation Method of Substation Equipment Infrared Image" Infrared Technology 38(09): 770–773.
 J. Frost, K. Keller, J. Lowe, T. Skeete, S. Walton, J. Castille, and N. Pal, (2013) “A note on interval estimation of the standard deviation of a gamma population with applications to statistical quality control" Applied Mathematical Modelling 37(4): 2580–2587. DOI: 10.1016/j.apm.2012.05.027.
 F. J. Franco, J. A. Clemente, M. Baylac, S. Rey, F. Villa, H. Mecha, J. A. Agapito, H. Puchner, G. Hubert, and R. Velazco, (2017) “Statistical deviations from the theoretical only-SBU model to estimate MCU rates in SRAMs" IEEE Transactions on Nuclear Science 64(8): 2152–2160. DOI: 10.1109/TNS.2017.2726938.
 E. J. Candes, M. B. Wakin, and S. P. Boyd, (2008) “Enhancing sparsity by reweighted ℓ 1 minimization" Journal of Fourier analysis and applications 14(5): 877–905.
 P. Tseng, (2001) “Convergence of a block coordinate descent method for nondifferentiable minimization" Journal of optimization theory and applications 109(3): 475–494. DOI: 10.1023/A:1017501703105.
 Q. Shi, S. Yin, K. Wang, L. Teng, and H. Li, (2021) “Multichannel convolutional neural network-based fuzzy active contour model for medical image segmentation" Evolving Systems: 1–15. DOI: 10.1007/s12530-021-09392-3.
 A. Jisi, S. Yin, et al., (2021) “A new feature fusion network for student behavior recognition in education" Journal of Applied Science and Engineering 24(2): 133–140. DOI: 10.6180/jase.202104_24(2).0002.
 H. Ahmad, S. K. Kim, J. H. Park, and S. Y. Jung, (2022) “Development of two-phase flow regime map for thermally stimulated flows using deep learning and image segmentation technique" International Journal of Multiphase Flow 146: 103869. DOI: 10.1016/j.ijmultiphaseflow.2021.103869.
 S. Gite, A. Mishra, and K. Kotecha, (2022) “Enhanced lung image segmentation using deep learning" Neural Computing and Applications: 1–15. DOI: 10.1007/s00521-021-06719-8.
 X. Fu, B. Fang, M. Zhou, and S. Kwong, (2021) “Active contour driven by adaptively weighted signed pressure force combined with Legendre polynomial for image segmentation" Information Sciences 564: 327–342. DOI: 10.1016/j.ins.2021.02.019.
 Z. B. W. Yiquan and J. Shouxin, (2010) “Infrared image enhancement method based on stationary wavelet transformation and Retinex" Acta Optica Sinica: DOI: 10.3788/AOS20103010.2788.
 J. Sun, (2021) “Application of Image Segmentation Algorithm Based on Partial Differential Equation in Legal Case Text Classification" Advances in Mathematical Physics 2021: DOI: 10.1155/2021/4062200.
 Y. Feng, H. Zhao, X. Li, X. Zhang, and H. Li, (2017) “A multi-scale 3D Otsu thresholding algorithm for medical image segmentation" Digital Signal Processing 60: 186–199. DOI: 10.13229/j.cnki.jdxbgxb201701037.