With the acceleration of urbanization, the problem of throwing objects at high altitude becomes more and more serious, which brings great threat to people’s life and property safety. With the acceleration of urbanization, the problem of throwing objects at high altitude becomes more and more serious, which brings great threat to people’s lives and property safety. In order to solve this problem, image recognition-based monitoring and an early warning system for high-altitude parabolic objects is proposed. The system uses cameras and image recognition technology to realize real-time monitoring and early warning of high-altitude parabolic objects. The system uses cameras and image recognition technology to realize real-time monitoring and early warning of high-altitude parabolic objects. The high-altitude parabolic monitoring and early warning system based on image recognition technology uses cameras and image recognition technology to realize real-time monitoring and early warning of high-altitude parabolic objects. In general, the monitoring and early warning system based on image recognition technology is an efficient and practical solution, which can be applied to the monitoring of parabolic behaviors. In general, the monitoring and early warning system based on image recognition technology is an efficient and practical solution, which can effectively improve the level of urban safety management and reduce the safety risks brought by high-altitude throwing.
Cite this paper
Jiang, Y. , Cai, M. and Shen, Z. (2024). High Altitude Parabolic Monitoring and Early Warning System Based on Image Recognition Technology. Open Access Library Journal, 11, e1568. doi: http://dx.doi.org/10.4236/oalib.1111568.
Ying, J., Li, H., Yang, H. and Zheng, L.Q. (2022) GPK-YOLOv5s: Content-Aware Reassembly of Features and Self Attention for High Altitude Parabolic Detection and Tracking. MATEC Web of Conferences, 42, 363-379. https://doi.org/10.1051/matecconf/202236301012
Zhao, X.L., Yue, C., Yang, C., Wang, R., Zhang, H. and Chen, J.X. (2022) High-Altitude Parabolic Identification Technology for Urban Buildings. International Conference on Electronic Information Engineering, Big Data and Computer Technology, 42, 291-293. https://doi.org/10.1117/12.2635430
Dong, N.Q., Linus, E., Yang, Y.X. and McDonagh, S. (2024) Label-Efficient Object Detection via Region Proposal Network Pre-Training. Neurocomputing, 577, 678-680. https://doi.org/10.1016/j.neucom.2024.127376
Zhang, X.S., Griffin, R. and Haruna, I. (2024) Area in Circle: A Novel Evaluation Metric for Object Detection. Knowledge-Based Systems, 293, 451-478. https://doi.org/10.1016/j.knosys.2024.111684
Wang, F., Zhou, J.C., Yang, J.X. and Zhang, W.S. (2024) Improved YOLOv7 Model for Underwater Sonar Image Object Detection. Journal of Visual Communication and Image Representation, 100, 67-85. https://doi.org/10.1016/j.jvcir.2024.104124
Gao, Z.R., Shi, Y.L., Li, S. and Young, W.F. (2024) Self-Attention and Long-Range Relationship Capture Network for Underwater Object Detection. Journal of King Saud University - Computer and Information Sciences, 36, 381-401. https://doi.org/10.1016/j.jksuci.2024.101971
Zhou, J.J., Zhang, B.H., Yang, X.L. and Yue, J. (2023) YOLO-CIR: The Network Based on YOLO and ConvNeXt for Infrared Object Detection. Infrared Physics & Technology, 131, 433-498. https://doi.org/10.1016/j.infrared.2023.104703
Li, B., Li, Y.L., Zhou, X.S. and Dan, X. (2023) A Substation Rotational Object Detection Based on Multi-Scale Feature Fusion and Refinement. Energy and AI, 14, 65-82. https://doi.org/10.1016/j.egyai.2023.100294
