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Development of real-time video target recognition system based on convolutional neural network

  
21 mars 2025
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Liang, R., Zhi, H., & Kamruzzaman, M. M. (2019). Methods of moving target detection and behavior recognition in intelligent vision monitoring. Acta Microscopica, 28(4). Liang R. Zhi H. Kamruzzaman M. M. ( 2019 ). Methods of moving target detection and behavior recognition in intelligent vision monitoring . Acta Microscopica , 28 ( 4 ). Search in Google Scholar

Yin, X. C., Zuo, Z. Y., Tian, S., & Liu, C. L. (2016). Text detection, tracking and recognition in video: a comprehensive survey. IEEE Transactions on Image Processing, 25(6), 2752-2773. Yin X. C. Zuo Z. Y. Tian S. Liu C. L. ( 2016 ). Text detection, tracking and recognition in video: a comprehensive survey . IEEE Transactions on Image Processing , 25 ( 6 ), 2752 - 2773 . Search in Google Scholar

Lu, S., Wang, B., Wang, H., Chen, L., Linjian, M., & Zhang, X. (2019). A real-time object detection algorithm for video. Computers & Electrical Engineering, 77, 398-408. Lu S. Wang B. Wang H. Chen L. Linjian M. Zhang X. ( 2019 ). A real-time object detection algorithm for video . Computers & Electrical Engineering , 77 , 398 - 408 . Search in Google Scholar

Cao, W., Yuan, J., He, Z., Zhang, Z., & He, Z. (2018). Fast deep neural networks with knowledge guided training and predicted regions of interests for real-time video object detection. IEEE Access, 6, 8990-8999. Cao W. Yuan J. He Z. Zhang Z. He Z. ( 2018 ). Fast deep neural networks with knowledge guided training and predicted regions of interests for real-time video object detection . IEEE Access , 6 , 8990 - 8999 . Search in Google Scholar

Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., Meena, S. R., Tiede, D., & Aryal, J. (2019). Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection. Remote Sensing, 11(2), 196. Ghorbanzadeh O. Blaschke T. Gholamnia K. Meena S. R. Tiede D. Aryal J. ( 2019 ). Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection . Remote Sensing , 11 ( 2 ), 196 . Search in Google Scholar

Riyaz, S., Sankhe, K., Ioannidis, S., & Chowdhury, K. (2018). Deep learning convolutional neural networks for radio identification. IEEE Communications Magazine, 56(9), 146-152. Riyaz S. Sankhe K. Ioannidis S. Chowdhury K. ( 2018 ). Deep learning convolutional neural networks for radio identification . IEEE Communications Magazine , 56 ( 9 ), 146 - 152 . Search in Google Scholar

Michelucci, U. (2019). Advanced applied deep learning: convolutional neural networks and object detection.Apress. Michelucci U. ( 2019 ). Advanced applied deep learning: convolutional neural networks and object detection . Apress . Search in Google Scholar

Aloysius, N., & Geetha, M. (2017, April). A review on deep convolutional neural networks. In 2017 international conference on communication and signal processing (ICCSP) (pp. 0588-0592). IEEE. Aloysius N. Geetha M. ( 2017 , April ). A review on deep convolutional neural networks . In 2017 international conference on communication and signal processing (ICCSP) (pp. 0588 - 0592 ). IEEE . Search in Google Scholar

Radovic, M., Adarkwa, O., & Wang, Q. (2017). Object recognition in aerial images using convolutional neural networks. Journal of Imaging, 3(2), 21. Radovic M. Adarkwa O. Wang Q. ( 2017 ). Object recognition in aerial images using convolutional neural networks . Journal of Imaging , 3 ( 2 ), 21 . Search in Google Scholar

Heo, S., Cho, S., Kim, Y., & Kim, H. (2020, April). Real-time object detection system with multi-path neural networks. In 2020 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS) (pp. 174-187). IEEE. Heo S. Cho S. Kim Y. Kim H. ( 2020 , April ). Real-time object detection system with multi-path neural networks . In 2020 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS) (pp. 174 - 187 ). IEEE . Search in Google Scholar

Huang, F., Wang, Y., Shen, X., Li, G., & Yan, S. (2012). Analysis of space target detection range based on space-borne fisheye imaging system in deep space background. Infrared Physics & Technology, 55(6), 475-480. Huang F. Wang Y. Shen X. Li G. Yan S. ( 2012 ). Analysis of space target detection range based on space-borne fisheye imaging system in deep space background . Infrared Physics & Technology , 55 ( 6 ), 475 - 480 . Search in Google Scholar

Zhou, Y., Zhu, W., He, Y., & Li, Y. (2023, May). Yolov8-based spatial target part recognition. In 2023 IEEE 3rd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA) (Vol. 3, pp. 1684-1687). IEEE. Zhou Y. Zhu W. He Y. Li Y. ( 2023 , May ). Yolov8-based spatial target part recognition . In 2023 IEEE 3rd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA) (Vol. 3 , pp. 1684 - 1687 ). IEEE . Search in Google Scholar

Li, N., Gong, C., Zhao, H., & Ma, Y. (2023). Space target material identification based on graph convolutional neural network. Remote Sensing, 15(7), 1937. Li N. Gong C. Zhao H. Ma Y. ( 2023 ). Space target material identification based on graph convolutional neural network . Remote Sensing , 15 ( 7 ), 1937 . Search in Google Scholar

Lin, B., Wang, J., Wang, H., Zhong, L., Yang, X., & Zhang, X. (2023). Small space target detection based on a convolutional neural network and guidance information. Aerospace, 10(5), 426. Lin B. Wang J. Wang H. Zhong L. Yang X. Zhang X. ( 2023 ). Small space target detection based on a convolutional neural network and guidance information . Aerospace , 10 ( 5 ), 426 . Search in Google Scholar

Sörensen, L. K., Bohté, S. M., De Jong, D., Slagter, H. A., & Scholte, H. S. (2023). Mechanisms of human dynamic object recognition revealed by sequential deep neural networks. PLOS Computational Biology, 19(6), e1011169. Sörensen L. K. Bohté S. M. De Jong D. Slagter H. A. Scholte H. S. ( 2023 ). Mechanisms of human dynamic object recognition revealed by sequential deep neural networks . PLOS Computational Biology , 19 ( 6 ), e1011169 . Search in Google Scholar

Seeliger, K., Fritsche, M., Güçlü, U., Schoenmakers, S., Schoffelen, J. M., Bosch, S. E., & Van Gerven, M. A. J. (2018). Convolutional neural network-based encoding and decoding of visual object recognition in space and time. NeuroImage, 180, 253-266. Seeliger K. Fritsche M. Güçlü U. Schoenmakers S. Schoffelen J. M. Bosch S. E. Van Gerven M. A. J. ( 2018 ). Convolutional neural network-based encoding and decoding of visual object recognition in space and time . NeuroImage , 180 , 253 - 266 . Search in Google Scholar

Wang, P., Chen, T., Ding, J., Pan, M., & Tang, S. (2022). Intelligent radar HRRP target recognition based on CNN-BERT model. EURASIP Journal on Advances in Signal Processing, 2022(1), 89. Wang P. Chen T. Ding J. Pan M. Tang S. ( 2022 ). Intelligent radar HRRP target recognition based on CNN-BERT model . EURASIP Journal on Advances in Signal Processing , 2022 ( 1 ), 89 . Search in Google Scholar

Pei, J., Huang, Y., Huo, W., Zhang, Y., Yang, J., & Yeo, T. S. (2017). SAR automatic target recognition based on multiview deep learning framework. IEEE Transactions on Geoscience and Remote Sensing, 56(4), 2196-2210. Pei J. Huang Y. Huo W. Zhang Y. Yang J. Yeo T. S. ( 2017 ). SAR automatic target recognition based on multiview deep learning framework . IEEE Transactions on Geoscience and Remote Sensing , 56 ( 4 ), 2196 - 2210 . Search in Google Scholar

Benali Amjoud, A., & Amrouch, M. (2020). Convolutional neural networks backbones for object detection. In Image and Signal Processing: 9th International Conference, ICISP 2020, Marrakesh, Morocco, June 4–6, 2020, Proceedings 9 (pp. 282-289). Springer International Publishing. Benali Amjoud A. Amrouch M. ( 2020 ). Convolutional neural networks backbones for object detection . In Image and Signal Processing: 9th International Conference, ICISP 2020, Marrakesh, Morocco, June 4–6, 2020, Proceedings 9 (pp. 282 - 289 ). Springer International Publishing . Search in Google Scholar

Gilan, A. A., Emad, M., & Alizadeh, B. (2019). FPGA-based implementation of a real-time object recognition system using convolutional neural network. IEEE Transactions on Circuits and Systems II: Express Briefs, 67(4), 755-759. Gilan A. A. Emad M. Alizadeh B. ( 2019 ). FPGA-based implementation of a real-time object recognition system using convolutional neural network . IEEE Transactions on Circuits and Systems II: Express Briefs , 67 ( 4 ), 755 - 759 . Search in Google Scholar

Kumar, A., & Srivastava, S. (2020). Object detection system based on convolution neural networks using single shot multi-box detector. Procedia Computer Science, 171, 2610-2617. Kumar A. Srivastava S. ( 2020 ). Object detection system based on convolution neural networks using single shot multi-box detector . Procedia Computer Science , 171 , 2610 - 2617 . Search in Google Scholar

KR, S. C. (2017, April). Real time object identification using deep convolutional neural networks. In 2017 International Conference on Communication and Signal Processing (ICCSP) (pp. 1801-1805). IEEE. KR S. C. ( 2017 , April ). Real time object identification using deep convolutional neural networks . In 2017 International Conference on Communication and Signal Processing (ICCSP) (pp. 1801 - 1805 ). IEEE . Search in Google Scholar

Patel, S., & Patel, A. (2021). Object detection with convolutional neural networks. Machine Learning for Predictive Analysis: Proceedings of ICTIS 2020, 529-539. Patel S. Patel A. ( 2021 ). Object detection with convolutional neural networks . Machine Learning for Predictive Analysis: Proceedings of ICTIS 2020 , 529 - 539 . Search in Google Scholar

Zhang, Y., Shen, L., Wang, X., & Hu, H. M. (2020, August). Drone video object detection using convolutional neural networks with time domain motion features. In 2020 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) (pp. 153-156). IEEE. Zhang Y. Shen L. Wang X. Hu H. M. ( 2020 , August ). Drone video object detection using convolutional neural networks with time domain motion features . In 2020 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) (pp. 153 - 156 ). IEEE . Search in Google Scholar

Alom, M. Z., Hasan, M., Yakopcic, C., Taha, T. M., & Asari, V. K. (2020). Improved inception-residual convolutional neural network for object recognition. Neural Computing and Applications, 32(1), 279-293. Alom M. Z. Hasan M. Yakopcic C. Taha T. M. Asari V. K. ( 2020 ). Improved inception-residual convolutional neural network for object recognition . Neural Computing and Applications , 32 ( 1 ), 279 - 293 . Search in Google Scholar

Nandhini, T. J., & Thinakaran, K. (2023, February). An Improved Crime Scene Detection System Based on Convolutional Neural Networks and Video Surveillance. In 2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT) (pp. 1-6). IEEE. Nandhini T. J. Thinakaran K. ( 2023 , February ). An Improved Crime Scene Detection System Based on Convolutional Neural Networks and Video Surveillance . In 2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT) (pp. 1 - 6 ). IEEE . Search in Google Scholar

Kang, K., Li, H., Yan, J., Zeng, X., Yang, B., Xiao, T., … & Ouyang, W. (2017). T-cnn: Tubelets with convolutional neural networks for object detection from videos. IEEE Transactions on Circuits and Systems for Video Technology, 28(10), 2896-2907. Kang K. Li H. Yan J. Zeng X. Yang B. Xiao T. Ouyang W. ( 2017 ). T-cnn: Tubelets with convolutional neural networks for object detection from videos . IEEE Transactions on Circuits and Systems for Video Technology , 28 ( 10 ), 2896 - 2907 . Search in Google Scholar

Galvez, R. L., Bandala, A. A., Dadios, E. P., Vicerra, R. R. P., & Maningo, J. M. Z. (2018, October). Object detection using convolutional neural networks. In TENCON 2018-2018 IEEE region 10 conference (pp. 2023-2027). IEEE. Galvez R. L. Bandala A. A. Dadios E. P. Vicerra R. R. P. Maningo J. M. Z. ( 2018 , October ). Object detection using convolutional neural networks . In TENCON 2018-2018 IEEE region 10 conference (pp. 2023 - 2027 ). IEEE . Search in Google Scholar

Qin, L., Yu, N., & Zhao, D. (2018). Applying the convolutional neural network deep learning technology to behavioural recognition in intelligent video. Tehnički vjesnik, 25(2), 528-535. Qin L. Yu N. Zhao D. ( 2018 ). Applying the convolutional neural network deep learning technology to behavioural recognition in intelligent video . Tehnički vjesnik , 25 ( 2 ), 528 - 535 . Search in Google Scholar

Nan Guofang & Ding Di. (2023). Diagnosis of rotating machinery based on improved convolutional neural networks with gray-level transformation. Journal of Vibroengineering(5),895-907. Guofang Nan Di Ding ( 2023 ). Diagnosis of rotating machinery based on improved convolutional neural networks with gray-level transformation . Journal of Vibroengineering ( 5 ), 895 - 907 . Search in Google Scholar

Sheng Wu,Chaolan Zhang,Chao Liu & Yuqiang Jin. (2024). Underwater target recognition algorithm based on improved convolutional neural network: YOLOV5-Improved. Journal of Physics: Conference Series(1). Wu Sheng Zhang Chaolan Liu Chao Jin Yuqiang ( 2024 ). Underwater target recognition algorithm based on improved convolutional neural network: YOLOV5-Improved . Journal of Physics: Conference Series ( 1 ). Search in Google Scholar

Yu Feiyang,Zhang Guoxiang,Zhao Feiyu,Wang Xiaoxuan,Liu Huan,Lin Ping & Chen Yongming. (2023). Improved YOLO-v5 model for boosting face mask recognition accuracy on heterogeneous IoT computing platforms.Internet of Things. Feiyang Yu Guoxiang Zhang Feiyu Zhao Xiaoxuan Wang Huan Liu Ping Lin Yongming Chen ( 2023 ). Improved YOLO-v5 model for boosting face mask recognition accuracy on heterogeneous IoT computing platforms . Internet of Things . Search in Google Scholar

, G. Kalyani,B. Janakiramaiah,L. V. Narasimha Prasad,A. Karuna & A. Mohan Babu. (2024). Retraction Note: Efficient crowd counting model using feature pyramid network and ResNeXt. Soft Computing(suppl 2),1-1. Kalyani G. Janakiramaiah B. Prasad L. V. Narasimha Karuna A. Babu A. Mohan ( 2024 ). Retraction Note: Efficient crowd counting model using feature pyramid network and ResNeXt . Soft Computing (suppl 2 ), 1 - 1 . Search in Google Scholar

Wang Sike,Dong Qiao,Chen Xueqin,Chu Zepeng,Li Ruiqi,Hu Jing & Gu Xingyu. (2024). Measurement of Asphalt Pavement Crack Length Using YOLO V5-BiFPN. Journal of Infrastructure Systems(2). Sike Wang Qiao Dong Xueqin Chen Zepeng Chu Ruiqi Li Jing Hu Xingyu Gu ( 2024 ). Measurement of Asphalt Pavement Crack Length Using YOLO V5-BiFPN . Journal of Infrastructure Systems ( 2 ). Search in Google Scholar