Machine Learning Methods for Analysis of Photo/Video Files from Cameras
Akylbekov O. Alshynov Sh. Tulegenova A. Ramazanova L. Baimukanova Zh.
2025Centre for Environment and Socio-Economic Research Publications
International Journal of Artificial Intelligence
2025#23Issue 240 - 56 pp.
The research relevance is determined by the need to improve the accuracy and speed of image segmentation in complex urban environments for automated monitoring systems. The study aimed to develop and evaluate the effectiveness of deep neural networks for solving the problem of semantic segmentation of photo and video data obtained from surveillance cameras. The study tested DeepLabv3+ and U-Net architectures adapted for real-time image processing. Data augmentation methods, including adaptive contrast enhancement and illumination normalisation, have been implemented to improve the algorithms resistance to adverse lighting conditions and weather factors. Experimental results on the Cityscapes and ADE20K datasets showed that the DeepLabv3+ model achieved an average IoU of 0.73 on the test data, and the use of optimised post-processing mechanisms reduced the accuracy drop to IoU = 0.65 in difficult shooting conditions. The image processing speed was 40 ms per frame, making the model suitable for use in real-time systems. The obtained results confirm the effectiveness of the proposed architectures and emphasise the need for further optimisation of the algorithms to improve the segmentation accuracy in low-light conditions. The practical significance of the study is to increase the reliability of traffic monitoring and public safety in the urban environment. Copyright
automated surveillance systems , dataset , Deep neural networks , DeepLabv3+ , PyTorch , semantic image segmentation , U-Net architecture , urban environment monitoring , video analytics , Vision Transformers
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Department of Software Engineering, Satbayev University, Almaty, Kazakhstan
Department of Computer Engineering, Astana It University, Astana, Kazakhstan
School of Engineering and Digital Sciences, Nazarbayev University, Almaty, Kazakhstan
Department of Pedagogy, Psychology and Primary Education, K. Zhubanov Aktobe Regional University, Aktobe, Kazakhstan
Department of Software Engineering
Department of Computer Engineering
School of Engineering and Digital Sciences
Department of Pedagogy
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