现代电子技术2024，Vol.47Issue(1)：55-61,7.DOI:10.16652/j.issn.1004-373x.2024.01.010
改进YOLOv7-tiny的手语识别算法研究
Research on sign language recognition algorithm based on improved YOLOv7-tiny
摘要
Abstract
When people need to interact with deaf individuals,they often face barriers in communication.To address this issue,an improved sign language recognition network model based on the YOLOv7-tiny has been proposed.The improvements aim at enhancing the accuracy and speed of the model.The channel domain of the CBAM attention mechanism is improved to eliminate the channel information loss caused by dimension reduction.The improved CBAM is added to the Neck layer of YOLOv7-tiny to enable the model to locate and recognize the key targets more accurately.The traditional CIoU boundary box loss function is replaced by the SIoU boundary box loss function to improve localization accuracy while accelerating boundary box regression.In addition,to reduce computation and accelerate detection speed,the normal convolution modules in the Neck layer are replaced with Ghost convolution modules.Experimental tests have shown that the mean average precision(mAP),precision rate,and recall rate of the improved network model have increased by 5.31％,6.53％ and 2.73％,respectively,effectively improving the detection accuracy of the sign language recognition network.关键词
手语识别/YOLOv7-tiny/Ghost卷积/注意力机制/SIoU/边界框Key words
sign language recognition/YOLOv7-tiny/Ghost convolution/attention mechanism/SIoU/boundary box分类
电子信息工程引用本文复制引用
韩晓冰,胡其胜,赵小飞,秋强..改进YOLOv7-tiny的手语识别算法研究[J].现代电子技术,2024,47(1):55-61,7.基金项目
陕西省重点研发计划(2023-YBGY-255) （2023-YBGY-255）
陕西省科技厅工业公关(2022GY-155) （2022GY-155）
