We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Real-Time Hand Detection Method Based on Lightweight Network.
- Authors
JIN Fangrui; WANG Yangping; YONG Jiu
- Abstract
Aiming at the hand detection method based on deep learning, the network model has complex structure, slow detection speed and serious memory consumption, which is difficult to meet the needs of real-time human-computer inter-action at the mobile terminal, a lightweight network model is proposed to realize the real-time detection of hand. The model is based on SSD. Firstly, the lightweight network MobileNetV2 is used as the backbone feature extraction network of the model to reduce the amount of parameters and computational complexity of the model. Then, aiming at the problem of insufficient feature extraction, a multi-scale convolution and feature fusion module is proposed and applied to three prediction feature layers to increase the adaptability of the network to different scale features by connecting convolution kernels of different sizes. Finally, the candidate box acquisition method is improved. The K-means++ clustering algorithm is used to adaptively generate the candidate box suitable for the hand, and the hand is accurately located to improve the detection accuracy of the model. In order to verify the effectiveness of the proposed method, relevant experiments are carried out on two public data sets, Ego Hands and Oxford Hand. The experimental results show that the accuracy of this method on the two data sets is 96.56% and 73.56% respectively, the detection speed is 45.4 FPS and 41.2 FPS, and the memory occupied by the model is only 19.5 MB. Finally, the algorithm is deployed on the mobile terminal for testing, and the results show that this method can provide a lightweight method for hand detection.
- Subjects
DEEP learning; FEATURE extraction; COMPUTATIONAL complexity; HAND
- Publication
Journal of Computer Engineering & Applications, 2023, Vol 59, Issue 14, p192
- ISSN
1002-8331
- Publication type
Article
- DOI
10.3778/j.issn.1002-8331.2204-0084