Real-Time Wheat Unsound Kernel Classification Detection Based on Improved YOLOv5

Abstract

China is one of the largest wheat production countries in the world. The wheat quality determines the price and many other aspects. The detection methods of wheat quality mainly depend on manual labor. It costs high amount of manpower and time, and the classification results are partly affected by different individuals. With the development of machine vision, an automatic classification system was presented in this study. A wheat unsound kernel identification method based on the improved YOLOv5 algorithm was designed by adding efficient channel attention (ECA). Compared with convolutional block attention module (CBAM) and squeeze-and-excitation network (SENet), the improved YOLOv5 algorithm was selected to fit the model better. The recognition results showed that YOLOv5 with the addition of the attention mechanism had a significant improvement in average accuracy over that without it. The most significant improvement was observed with the addition of ECA-YOLOv5, with an average accuracy of 96.24%, a 10% improvement over the other two models, and a 13% improvement over the original YOLOv5. This satisfied the application requirements for detection of wheat unsound kernel.