Web Based Image Processing And Enhancement Using Flask

To facilitate multi-object detection and recognition tasks on the Intelligent Roadside Surveillance System, we propose an efficient flowchart for night image enhancement by employing multi-source data fusion. Using a fixed field of view and easy multi-sensor fusion, we present a complete set of multi-sensor fusion schemes suitable for the intelligent roadside system. The two key fusions are the decision-level fusion of camera and radar and the pixel-level fusion of day and night images.

[1]            P. D. Macheret, R. Savchuk, and G. Shkuratov, “Intelligent transport systems: Analysis of the current state and prospects of development,” 2021 International Conference on Quality Management, Transport and Information Security, Information Technologies, 2021. [2]              J. Mao, D. Hong, X. Wang, C. H. Hsu, and A. Shanthini, “Cvrrss-chd: Computer vision-related roadside surveillance system using compound hierarchical-deep models,” IET Intelligent Transport Systems, vol. 14, no. 11, pp. 1353–1362, 2020. [3]              S. T. T and S. M., “Nighttime visual refinement techniques for surveillance video: a review,” p. 32137-32158, 2019. [4]            G. Sahu, A. Seal, D. Bhattacharjee, M. Nasipuri, P. Brida, and O. Krejcar, “Trends and prospects of techniques for haze removal from degraded images: A survey,” IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 6, no. 4, pp. 762–782, 2022. [5]              G. Sahu, A. Seal, O. Krejcar, and A. Yazidi, “Single image dehazing using a new color channel,” Journal of Visual Communication and Image Representation, vol. 74, p. 103008, 2021. [6]            G. Sahu, A. Seal, A. Yazidi, and O. Krejcar, “A dual-channel dehazenet for single image dehazing in visual internet of things using pynq-z2 board,” IEEE Transactions on Automation Science and Engineering, pp. 1–15, 2022. [7]              J. Bai, S. Li, L. Huang, and H. Chen, “Robust detection and tracking method for moving object based on radar and camera data fusion,” IEEE Sensors Journal, vol. PP, no. 99, pp. 1–1, 2021. [8]              P. Liu, G. Yu, Z. Wang, B. Zhou, and P. Chen, “Object classification based on enhanced evidence theory: Radar-vision fusion approach for roadside application,” IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1–12, 2022. [9]            M. Gao, J. Wang, Y. Chen, C. Du, and Y. Zeng, “An improved multiexposure image fusion method for intelligent transportation system,” Electronics, vol. 10, no. 4, p. 383, 2021. [10]              M. Bouain, D. Berdjag, N. Fakhfakh, and R. B. Atitallah, “Multi-sensor fusion for obstacle detection and recognition: A belief-based approach,” in 2018 21st International Conference on Information Fusion (FUSION), 2018, pp. 1217–1224.