Research on the optimization of computer vision algorithms for large - scale models
Haolan Li
Western Institute of Computing Technology
DOI: https://doi.org/10.59429/esta.v12i1.9656
Keywords: Computer vision; Large-scale models; Algorithm optimization; Model compression; Acceleration
Abstract
This paper delves into the optimization of computer vision algorithms for large - scale models. With the rapid development of deep learning in computer vision, large - scale models have shown remarkable performance in various tasks. However, their high computational demands and memory requirements pose significant challenges. This research explores techniques such as model compression, acceleration strategies, and efficient data handling to optimize these algorithms. Through the analysis, it demonstrates the effectiveness of these optimization methods in improving the efficiency and performance of large - scale computer vision models, making them more applicable in real - world scenarios.
References
[1] Yang L ,Driscol J ,Gong M , et al. TGGLinesPlus: A Robust Topological Graph‐Guided Computer Vision Algorithm for Line Detection From Images [J]. Transactions in GIS, 2025, 29 (1): e70015-e70015.
[2] Tian Y ,Zhu F . Application of computer vision algorithm in ceramic surface texture analysis and prediction [J]. Intelligent Systems with Applications, 2025, 25 200482-200482.
[3] Liu W ,Chen J ,Lyu Z , et al. Automatic tile position and orientation detection combining deep-learning and rule-based computer vision algorithms [J]. Automation in Construction, 2025, 171 106001-106001.
[4] Zhang H . An innovative approach to lighting design: implementing computer vision algorithms for dynamic light environments [J]. International Journal of System Assurance Engineering and Management, 2025, (prepublish): 1-13.