Detection method of heterotropic fiber based on improved YOLOv5

Abstract

Aiming at the problems of inaccuracy and poor real-time detection of heterosexual fiber in cotton cleaning process, a target detection model of heterosexual fiber based on YOLOv5 network was proposed to realize fast and accurate identification and location of heterosexual fiber in cotton. YOLOv5 was selected as the basic target detection model, and depth separable convolution was introduced to reduce the number of parameters of the detection model and improve the detection speed. Combined with SE module of channel attention mechanism, it can reduce irrelevant information interference and enhance feature expression ability. Comparative ablation tests were performed on YOLOv5 model before and after modification. The experimental results show that the improved YOLOv5 model has a mAP of 91.6% and a frame rate of 83 frames /s. The improved YOLOv5 model can not only improve the detection accuracy but also improve the detection speed, which can better meet the requirements of accuracy and real-time detection of cotton foreign fiber.