在人机协作缝纫中,实现人机交流互动的前提是机器人对工人的缝纫手势的检测与理解。针对传统算法手势识别率低、小目标手势检测效果差的问题,提出一种基于改进单发检测(SSD)模型的缝纫手势识别方法。首先引入更深的Resnet50残差网络替换原始SSD模型中的VGG16基础网络,改善网络特征提取能力。然后采用基于特征金字塔(FPN)的网络结构进行高低层特征融合,进一步提高了检测精度。实验结果表明,在构建的缝纫手势数据集中,通过对比原始SSD算法及其他算法,发现改进模型的检测精度显著提升;网络中残差连接在提高精度的同时并没有增加模型的参数和复杂程度,每幅图片的平均检测速度为52 frame/s,完全满足缝纫手势的实时检测。

In human-robot collaborative sewing, the premise of realizing human-robot interaction is based on the detection and understanding of robot sewing gestures. The traditional algorithm is characterized by a low gesture-recognition rate and poor target-gesture detection. Therefore, a method based on an improved single-shot multibox detector (SSD) model for recognizing sewing gestures is proposed in this work. First, a deeper Resnet50 residual network was introduced to replace the VGG16 basic network in the original SSD model to improve the network feature extraction capability. Subsequently, a feature base pyramid (FPN)-based network structure was used to perform a fusion of high- and low-level features, thereby further improving the detection accuracy. Experiment results reveal that for the constructed sewing gesture dataset, the improved model exhibited higher detection accuracy than the original SSD algorithm and other algorithms. Furthermore, the residual connection in the network improved accuracy without increasing the number of parameters and complexity of the model. In our method, the average detection speed is 52 frame/s, which can fully meet the requirements for real-time detection of sewing gestures.