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Convergence property of Nesterov-accelerated adaptive moment estimation with safety helmet detection and classification in smart industry application
Author(s)
Jirakitpuwapat, Wachirapong
Dubey, Premnath
Prasertsuk, Narachata
Phanthong, Chaowarit
Tritham, Chatchai
Tritham, Chattabhorn
Chandharakool, Somprattana
Tharathep, Chanvit
Soontornpipit, Pichitpong
Date Issued
November 15, 2024
Type
Article
Abstract
We propose a technique for first-order gradient-based optimization of stochastic objective functions called Nesterov-accelerated adaptive moment assessment, which makes use of dynamic evaluations of lower-order moments. The adaptive moment assessment and the Nesterov acceleration gradient are combined. Consequently, it has perks, and this technique is convenient to use, numerically economical, memory-light, and very well-suited for challenges with massive amounts of information and characteristics. Additionally, we investigate the algorithm's convergence characteristics and propose a conservative constraint on the convergence rate. Finally, we employ this technique for the detection and classification of safety helmets.
Citation
Mathematical Methods in the Applied Sciences, 47(16), 12650-12667, 2024