Multi-objective neural architecture search combining binary artificial bee colony algorithm for dynamic hand gesture recognition

Designing neural network architectures for dynamic hand gesture recognition (DHGR) requires a careful balance between recognition accuracy and computational efficiency, particularly for real-time interaction on mobile or embedded platforms. To address this challenge, we propose MONAS_ABC, a multi-ob...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Expert systems with applications Ročník 299; s. 130128
Hlavní autoři: Ye, Tingyu, Zhang, Ping, Zeng, Hongliang, Wang, Jiahua
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.03.2026
Témata:
Artificial bee colony algorithm
Dynamic gesture recognition
Multi-objective
Neural architecture search
Neural network
Dynamic gesture recognition
Artificial bee colony algorithm
Neural network
Neural architecture search
Multi-objective
ISSN:0957-4174
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Designing neural network architectures for dynamic hand gesture recognition (DHGR) requires a careful balance between recognition accuracy and computational efficiency, particularly for real-time interaction on mobile or embedded platforms. To address this challenge, we propose MONAS_ABC, a multi-objective neural architecture search (NAS) framework based on the binary artificial bee colony algorithm. The framework incorporates tailored strategies across the employed, onlooker, and scout bee phases, enabling efficient exploration in a MobileNetV2-inspired lightweight search space through binary-encoded representations. We first evaluate MONAS_ABC on two multi-objective optimization benchmarks, C10/MOP and IN1K/MOP, where it demonstrates superior convergence behavior and solution diversity compared to conventional approaches. We further apply the framework to two real-world DHGR datasets: EgoGesture and NvGesture. On EgoGesture, MONAS_ABC achieves a Top-1 accuracy of 93.17 % with only 0.76G FLOPs and 2.05 million parameters, significantly outperforming established 3D CNN models such as C3D and TSM in both accuracy and resource consumption. Comparable performance is observed on NvGesture, confirming the generalizability of the discovered architectures. These results collectively demonstrate that MONAS_ABC effectively discovers scalable and efficient architectures, capable of balancing performance and complexity across both generic optimization problems and practical DHGR scenarios.
ISSN:0957-4174
DOI:10.1016/j.eswa.2025.130128