Research on computer vision technology based on BP-LSTM hybrid network

[1] Chen C, Li D. Research on the Detection and Tracking Algorithm of Moving Object in Image Based on Computer Vision Technology [J]. Wireless Communications and Mobile Computing, 2021, 2021(4):1-7. Search in Google Scholar

[2] Li Z, Guo R, Li M, et al. A review of computer vision technologies for plant phenotyping[J]. Computers and Electronics in Agriculture, 2020, 176:105672. Search in Google Scholar

[3] Chen D. The Development and Application of Computer Vision Technology [J]. Computer Knowledge and Technology, 2008. Search in Google Scholar

[4] Shekokar N, Kasat A, Jain S, et al. Shop and Go: An innovative approach towards shopping using Deep Learning and Computer Vision[C]//2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT). 2020. Search in Google Scholar

[5] Ji Q, Zheng E, Yue X, et al. Face recognition method based on HOG and DMMA from single training sample [J]. Multimedia Tools & Applications, 2016, 75(21):13163-13177. Search in Google Scholar

[6] Kumar P, Henikoff S, Ng P C. Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm.[J]. Nature Protocols, 2009, 4(8):1073-1081. Search in Google Scholar

[7] Wang X, Han T X, Yan S. An HOG-LBP human detector with partial occlusion handling[C]//2009 IEEE 12th International Conference on Computer Vision. IEEE, 2010. Search in Google Scholar

[8] Ren S, He K, Girshick R, et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks [J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, 39(6):1137-1149. Search in Google Scholar

[9] Li J, Liang X, Shen S, et al. Scale-Aware Fast R-CNN for Pedestrian Detection [J]. IEEE Transactions on Multimedia, 2017. Search in Google Scholar

[10] Zhang Y, Qian T, Tang W. Buildings-to-distribution-network integration considering power transformer loading capability and distribution network reconfiguration [J]. Energy, 2022, 244. Search in Google Scholar

[11] CH FANG, YN TAO, JG EANG, et al. Mapping Relation of Leakage Currents of Polluted Insulators and Discharge Arc Area [J]. Frontiers in Energy Research, 2021. Search in Google Scholar

[12] Badrinarayanan V, Kendall A, Cipolla R. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation [J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017:1-1. Search in Google Scholar

[13] Chowdary E. Impact Of Machine Learning Models In Pneumonia Diagnosis With Features Extracted From Chest X-Rays Using VGG16 [J]. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021, 12(5):1521-1530. Search in Google Scholar

[14] Alom M Z, Taha T M, Yakopcic C, et al. The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches [J]. 2018. Search in Google Scholar

[15] Ke H, D Chen, Li X, et al. Towards Brain Big Data Classification: Epileptic EEG Identification with a Lightweight VGGNet on Global MIC [J]. IEEE Access, 2018:1-1. Search in Google Scholar

[16] A H C, A Q D, A L Y, et al. VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images – ScienceDirect [J]. NeuroImage, 2018, 170:446-455. Search in Google Scholar

[17] Zhang Z, Liang X, Xu D, et al. A Sparse-View CT Reconstruction Method Based on Combination of DenseNet and Deconvolution[J]. IEEE Transactions on Medical Imaging, 2018, 37(6):1-1. Search in Google Scholar

[18] T. Qian, Y. Liu, W. H Zhang, W. H. Tang*, M. Shahidehpour. Event-Triggered Updating Method in Centralized and Distributed Secondary Controls for Islanded Microgrid Restoration [J]. IEEE Transactions on Smart Gird, 2020, 11(2): 1387-1395. Search in Google Scholar

[19] T. Qian, Xingyu Chen, Yanli Xin, W. H. Tang, Lixiao Wang. Resilient Decentralized Optimization of Chance Constrained Electricity-gas Systems over Lossy Communication Networks [J]. Energy, 2022, 239, 122158. Search in Google Scholar

[20] Ma Y, Li L, Yin Z, et al. Research and application of network status prediction based on BP neural network for intelligent production line[J]. Procedia Computer Science, 2021, 183(20):189-196. Search in Google Scholar

[21] Liu H, Chen D, Lin F, et al. Wind Power Short-Term Forecasting Based on LSTM Neural Network With Dragonfly Algorithm[J]. Journal of Physics Conference Series, 2021, 1748(3):032015. Search in Google Scholar

[22] Li Y, Chen M, Zhao W. Investigating Long-term Vehicle Speed Prediction based on BP-LSTM Algorithms [J]. IET Intelligent Transport Systems, 2019, 13(8):1281-1290. Search in Google Scholar

[23] Korzhebin T A, Egorov A D. Comparison of Combinations of Data Augmentation Methods and Transfer Learning Strategies in Image Classification Used in Convolution Deep Neural Networks[C]//2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus). IEEE, 2021. Search in Google Scholar

[24] Sun J, Yu J. Robust and fast image processing method for feature extraction of jet engine modulation signals[C]//IET International Radar Conference (IET IRC 2020). 2021. Search in Google Scholar

[25] Sami R, Soltane S, Helal M. Microscopic Image Segmentation and Morphological Characterization of Novel Chitosan/Silica Nanoparticle/Nisin Films Using Antimicrobial Technique for Blueberry Preservation [J]. Membranes, 2021, 11(5):303. Search in Google Scholar