A Dual Detection Head YOLO Model With Its Application in Wheat Ear Recognition

The spike number(SN) per unit area is an important indicator for measuring wheat yield. The use of AI to detect and count wheat ears is increasingly valued by researchers. In order to improve the accuracy of wheat ear recognition, the model is deployed on limited mobile devices to achieve accurate estimation of wheat yield. This paper proposes a lightweight hybrid design network Wheat-YoloNet based on improved YOLOv8 for wheat ear recognition. On the basis of the YOLOv8 network structure, we only retain small and large object detection heads, reducing the number of network parameters while ensuring detection performance. We added CBAM attention mechanism module to Backbone to improve the feature extraction performance of the model and introduce Wise-IoU to balance the samples. Compared with the YOLOv8n baseline model, the improved model has reduced parameter count by 33.3%, outperforming other comparison object detection algorithms, and can be deployed and run on devices with limited computing power.