Abstract
In recent years, the rapid advancement of artificial intelligence (AI) has revolutionized various industries, with image processing playing an important role in the revolution. AI-powered image analysis has opened up new possibilities in healthcare diagnostics, autonomous navigation, and security surveillance, fuelling demand for sophisticated image-processing solutions. This paper describes an innovative approach to AI image processing that combines multi-sensor data fusion with convolutional neural networks (CNNs) within a fuzzy neural network framework. This integration uses data from various sensors, including cameras, lidar, and radar, to improve the robustness and precision of image analysis and interpretation. The T–S model serves as the foundation for the information fusion strategy. A comprehensive investigation of deep learning algorithms reveals inherent strengths such as robustness and parallelism. However, it also identifies limitations, particularly in image segmentation tasks, characterized by challenges like premature convergence and prolonged computation times. The paper proposes a quantization technique for deep learning algorithms to address these issues and introduces chaotic optimization to expedite convergence rates. It also presents a novel three-dimensional Otsu threshold segmentation method based on CNNs, which overcomes noise susceptibility in traditional two-dimensional approaches. Integrating Gray morphology and this three-dimensional Otsu threshold segmentation method results in the development of a three-dimensional Gray Otsu model. This model is the basis for designing a fitness function for the CNN algorithm, optimizing its efficacy. Experimental validation demonstrates the proposed algorithm’s effectiveness, achieving an impressive 91% accuracy rate while displaying robust noise resistance and versatility. Comparative assessments against other leading AI architectures, including multilayer perceptron, radial basis function network, recurrent neural network, and long short-term memory network, affirm the superior performance achieved by the proposed approach.
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References
Ali M, Yin B, Kunar A, Sheikh AM, Bilal H et al. (2020) Reduction of multiplications in convolutional neural networks. In: 2020 39th Chinese control conference (CCC). IEEE, pp 7406–7411. https://doi.org/10.23919/CCC50068.2020.9188843.
Aslam MS, Dai X, Hou J, Li Q, Ullah R, Ni Z, Liu Y (2020) Reliable control design for composite-driven scheme based on delay networked T–S fuzzy system. Int J Robust Nonlinear Control 30(4):1622–1642
Aslam MS, Qaisar I, Majid A, Shamrooz S (2023) Adaptive event-triggered robust H∞ control for Takagi–Sugeno fuzzy networked Markov jump systems with time-varying delay. Asian J Control 25(1):213–228
Bilal H, Yin B, Aslam MS et al (2023) A practical study of active disturbance rejection control for rotary flexible joint robot manipulator. Soft Comput 27:4987–5001. https://doi.org/10.1007/s00500-023-08026-x
Cao B, Zhao J, Gu Y, Fan S, Yang P (2019) Security-aware industrial wireless sensor network deployment optimization. IEEE Trans Ind Inf 16(8):5309–5316
Cheng B, Wang M, Zhao S, Zhai Z, Zhu D, Chen J (2017) Situation-aware dynamic service coordination in an IoT environment. IEEE/ACM Trans Netw 25(4):2082–2095
Cheng D, Chen L, Lv C, Guo L, Kou Q (2022) Light-guided and cross-fusion U-Net for anti-illumination image super-resolution. IEEE Trans Circuits Syst Video Technol 32(12):8436–8449
Cong R, Sheng H, Yang D, Cui Z, Chen R (2023) Exploiting spatial and angular correlations with deep efficient transformers for light field image super-resolution. IEEE Trans Multimed
Fan W, Yang L, Bouguila N (2021) Unsupervised grouped axial data modeling via hierarchical Bayesian nonparametric models with Watson distributions. IEEE Trans Pattern Anal Mach Intell 44(12):9654–9668
Fu C, Yuan H, Xu H, Zhang H, Shen L (2023) TMSO-Net: texture adaptive multi-scale observation for light field image depth estimation. J vis Commun Image Represent 90:103731
Han Y, Wang B, Guan T, Tian D, Yang G, Wei W, Tang H, Chuah JH (2022) Research on road environmental sense method of intelligent vehicle based on tracking check. IEEE Trans Intell Transp Syst 24(1):1261–1275
Hazrat B, Yin B, Kumar A, Ali M, Zhang J, Yao J (2023) Jerk-bounded trajectory planning for rotary flexible joint manipulator: an experimental approach. Soft Comput 27(7):4029–4039. https://doi.org/10.1007/s00500-023-07923-5
Kumar A, Shaikh AM, Li Y et al (2021) Pruning filters with L1-norm and capped L1-norm for CNN compression. Appl Intell 51:1152–1160. https://doi.org/10.1007/s10489-020-01894-y
Li B, Tan Y, Wu AG, Duan GR (2021) A distributionally robust optimization-based method for stochastic model predictive control. IEEE Trans Autom Control 67(11):5762–5776
Li Y, Qian J, Feng S, Chen Q, Zuo C (2022) Deep-learning-enabled dual-frequency composite fringe projection profilometry for single-shot absolute 3D shape measurement. Opto-Electron Adv 5(5):210021–210031
Li L, Wang P, Zheng X, Xie Q, Tao X, Velásquez JD (2023) Dual-interactive fusion for code-mixed deep representation learning in tag recommendation. Inf Fusion 99:101862
Liu H, Yuan H, Liu Q, Hou J, Zeng H, Kwong S (2021a) A hybrid compression framework for color attributes of static 3D point clouds. IEEE Trans Circuits Syst Video Technol 32(3):1564–1577
Liu Q, Yuan H, Hamzaoui R, Su H, Hou J, Yang H (2021b) Reduced reference perceptual quality model with application to rate control for video-based point cloud compression. IEEE Trans Image Process 30:6623–6636
Liu AA, Zhai Y, Xu N, Nie W, Li W, Zhang Y (2021c) Region-aware image captioning via interaction learning. IEEE Trans Circuits Syst Video Technol 32(6):3685–3696
Liu M, Zhang X, Yang B, Yin Z, Liu S, Yin L, Zheng W (2023) Three-dimensional modeling of heart soft tissue motion. Appl Sci 13(4):2493
Lu S, Liu S, Hou P, Yang B, Liu M, Yin L, Zheng W (2023a) Soft tissue feature tracking based on DeepMatching network. CMES Comput Model Eng Sci 136(1):363–379
Lu S, Yang B, Xiao Y, Liu S, Liu M, Yin L, Zheng W (2023b) Iterative reconstruction of low-dose CT based on differential sparse. Biomed Signal Process Control 79:104204
Lu S, Yang J, Yang B, Yin Z, Liu M, Yin L, Zheng W (2023c) Analysis and design of surgical instrument localization algorithm. CMES Comput Model Eng Sci 137(1):669
Ma X, Dong Z, Quan W, Dong Y, Tan Y (2023) Real-time assessment of asphalt pavement moduli and traffic loads using monitoring data from built-in sensors: optimal sensor placement and identification algorithm. Mech Syst Signal Process 187:109930
Mao Y, Zhu Y, Tang Z, Chen Z (2022) A novel airspace planning algorithm for cooperative target localization. Electronics 11(18):2950
Min H, Fang Y, Wu X, Lei X, Chen S, Teixeira R, Zhu B, Zhao X, Xu Z (2023) A fault diagnosis framework for autonomous vehicles with sensor self-diagnosis. Expert Syst Appl 224:120002
Shamrooz M, Li Q, Hou J (2021) Fault detection for asynchronous T–S fuzzy networked Markov jump systems with new event-triggered scheme. IET Control Theory Appl 15(11):1461–1473
Shen Y, Ding N, Zheng HT, Li Y, Yang M (2020) Modeling relation paths for knowledge graph completion. IEEE Trans Knowl Data Eng 33(11):3607–3617
Wang Y, Xu N, Liu AA, Li W, Zhang Y (2021) High-order interaction learning for image captioning. IEEE Trans Circuits Syst Video Technol 32(7):4417–4430
Wang W, Chen Z, Yuan X (2022a) Simple low-light image enhancement based on Weber-Fechner law in logarithmic space. Signal Process Image Commun 106:116742
Wang F, Wang H, Zhou X, Fu R (2022b) A driving fatigue feature detection method based on multifractal theory. IEEE Sens J 22(19):19046–19059
Wang L, Zhai Q, Yin B et al (2019) Second-order convolutional network for crowd counting. In: Proceedings of the SPIE 11198, fourth international workshop on pattern recognition, 111980T (31 July 2019). https://doi.org/10.1117/12.2540362
Wu Z, Cao J, Wang Y, Wang Y, Zhang L, Wu J (2018) hPSD: a hybrid PU-learning-based spammer detection model for product reviews. IEEE Trans Cybern 50(4):1595–1606
Xu H, Sun Z, Cao Y et al (2023) A data-driven approach for intrusion and anomaly detection using automated machine learning for the Internet of Things. Soft Comput. https://doi.org/10.1007/s00500-023-09037-4
Yang M, Wang H, Hu K, Yin G, Wei Z (2022) IA-Net: An inception–attention-module-based network for classifying underwater images from others. IEEE J Oceanic Eng 47(3):704–717
Yao W, Guo Y, Wu Y, Guo J (2017) Experimental validation of fuzzy PID control of flexible joint system in presence of uncertainties. In: 2017 36th Chinese control conference (CCC), pp 4192–4197. IEEE. https://doi.org/10.23919/ChiCC.2017.8028015
Yin B, Khan J, Wang L, Zhang J, Kumar A (2019) Real-time lane detection and tracking for advanced driver assistance systems. In: 2019 Chinese control conference (CCC). IEEE, pp 6772–6777. https://doi.org/10.23919/ChiCC.2019.8866334
Zhang J, Peng S, Gao Y, Zhang Z, Hong Q (2023a) APMSA: adversarial perturbation against model stealing attacks. IEEE Trans Inf Forensics Secur 18:1667–1679
Zhang C, Xiao P, Zhao ZT, Liu Z, Yu J, Hu XY, Chu HB, Xu JJ, Liu MY, Zou Q, Zhang L (2023b) A wearable localized surface plasmons antenna sensor for communication and sweat sensing. IEEE Sens J 23:11591
Zhao K, Jia Z, Jia F, Shao H (2023) Multi-scale integrated deep self-attention network for predicting remaining useful life of aero-engine. Eng Appl Artif Intell 120:105860
Zheng Y, Lv X, Qian L, Liu X (2022) An optimal bp neural network track prediction method based on a GA–ACO hybrid algorithm. J Mar Sci Eng 10(10):1399
Zhou G, Liu X (2022) Orthorectification model for extra-length linear array imagery. IEEE Trans Geosci Remote Sens 60:1–10
Zhou X, Zhang L (2022) SA-FPN: an effective feature pyramid network for crowded human detection. Appl Intell 52(11):12556–12568
Zhou L, Ye Y, Tang T, Nan K, Qin Y (2021) Robust matching for SAR and optical images using multi-scale convolutional gradient features. IEEE Geosci Remote Sens Lett 19:1–5
Zhou D, Sheng M, Li J, Han Z (2023) Aerospace integrated networks innovation for empowering 6G: a survey and future challenges. IEEE Commun Surv Tutor 25:975
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Zhang, W., Dong, M. & Jiang, L. Image segmentation using convolutional neural networks in multi-sensor information fusion. Soft Comput 27, 18353–18372 (2023). https://doi.org/10.1007/s00500-023-09271-w
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DOI: https://doi.org/10.1007/s00500-023-09271-w