Abstract
In order to help the hearing-impaired communicate with others in social life, promote the employment of the disabled, and do our best to improve the quality of life of the hearing-impaired, this project proposes a self-training hand model based on YOLOv5s, and adds CBAM attention mechanism to improve it, so as to improve the model recognition accuracy of the sign language interpretation machine to a certain extent. In view of the practical problem that the assistive tools that the hearing impaired people can choose in the market have certain limitations, this paper mainly introduces the basic structure and innovation points of the sign language interpreter researched by the project, the main application technology, the specific help for the hearing impaired and the development prospect of the sign language interpreter. Therefore, it is concluded that the sign language interpreter studied in this project has certain development prospects. The experimental results show that the average accuracy of the improved algorithm is 97.01%, which is 2.78 percentage points higher than that of the original YOLOv5s, and the number of parameters is not much different, which can meet the daily life Demand.
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