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| As the health and safety of wheat kernel quality is an important part of food security, the rapid and accurate detection of wheat kernel quality has always been the focus of attention. Some detection methods have been proposed in the last few years. However, these algorithms are incapable of meeting both the requirements of speed and accuracy simultaneously. In order to meet these requirements, we propose a YOLOv5s_BR2 model based on the improved YOLOv5 model. A dataset of 7844 wheat kernels, including mildew wheat kernels, gibberella wheat kernels, germinant wheat kernels and normal wheat kernels, is constructed. Using this dataset, we analyze and research the object detection algorithms for wheat kernel quality detection. Through optimization operations such as decoupling and de-branching of the Neck structure of the YOLOv5 model, we propose the YOLOv5s_BR2 model. Experimental results show that YOLOv5s_BR2 achieves 95.5% accuracy on four kinds of wheat kernels. The detection speed on the GTX1050 graphics card reaches 32.8FPS, which is an improvement of 17% compared with YOLOv5s, and the detection time of 100 grams of 2500 kernels is 19 seconds. YOLOv5s_BR2 meets the requirements of high efficiency, accuracy and reliability applied to the wheat kernel quality detection system. |
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| Keywords:deep learning; object detection; wheat kernel quality testing; wheat kernel dataset; improved YOLOv5 |
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