关键词: 缝纫手势识别, 目标检测, YOLO深度卷积神经网络, 服装缝纫, 人机协作

Abstract:

A method of detecting and recognizing sewing gestures based on YOLO deep convolutional neural network was proposed to solve the problems of similar and less recognizable gestures in complex environments in the field of human-machine cooperation. Four complex sewing gestures were used to detect objects and construct a sewing gesture data set. By adding dense connection layer in the deep network of YOLOv3 low resolution, image feature transmission and reuse were enhanced, network performance was improved, and end-to-end sewing gesture was realized. The experimental results show that the trained model mean average precision is 94.45%, the intersection ratio is 0.87, and the harmonic mean is 0.885. By comparing region-convolutional neural networks, YOLOv2 and the original YOLOv3 algorithm, the detection accuracy of the improved method is significantly improved. At the same time, in the case of GPU acceleration, the average detection speed is 43.0 frames/s, and this fully satisfies the real-time detection of sewing gestures, and provides an algorithm basis for recognizing sewing gestures in complex environments.

Key words: sewing gesture recognition, target detection, YOLO deep convolutional neural network, garment sewing, human-machine cooperation