Detection of Impurities in Wool Based on Improved YOlOV8

Abstract

In the current production process of wool products, the cleaning of wool raw materials has been realized in an automated way. However, detecting whether the washed and dried wool still contains excessive impurities still requires manual testing. This method greatly reduces production efficiency. To solve the problem of detecting wool impurities, we propose an improved model based on YOLOv8. Our work applied some techniques to solve the low resource model training problem, and incorporated a block for small object detection into the new neural network structure. The newly proposed model achieved an accuracy of 84.3% on the self built dataset and also achieved good results on the VisDrone2019 dataset.