Research on real-time cloth edge extraction method based on ENet semantic segmentation

A vision-based sewing trajectory extraction method aims at the difficulty of adaptive generation of sewing trajectory in garment automatic processing. Firstly, the ENet model is improved and the I-ENet semantic segmentation algorithm is proposed; Then, based on the semantic segmentation results, a dynamic edge extraction method based on the GaussMod fitting method is proposed. Based on segmented images, curve fitting, median filtering, and edge-preserving filtering are used. Finally, the Canny operator is used to find the sewing edge trajectory curve. At last, the sewing trajectory curve of the robot is generated. Through experimental verification, the MIoU and PA of the semantic segmentation algorithm I-ENet proposed in this paper reach 95.06% and 97.80% respectively. Compared with the MIoU of the original model of ENet, the MIoU is improved by 2.78%, the pixel accuracy is improved by 1.18%, and the frame rate is 30 FPS. It can realize cloth segmentation and extraction in an unstructured environment. The maximum error between the sewing edge trajectory and the actual edge is 1.35 mm, the mean error is 0.61 mm, and the mean value of dynamic variance is 0.57 mm. This method meets the practical application requirements.