Improved YOLOv8n based lotus seedpod detection algorithm

Abstract

These Aiming at the influence of the shape appearance, color and growth environment of lotus seedling, lotus seedling detection exists problems such as low efficiency, low precision, leakage and misdetection, etc., an improved lotus seedling detection algorithm FSM-YOLOv8 is proposed based on the YOLOv8n model. First, the C2f-Faster module reduces the number of model parameters while ensuring the structural feature extraction capability of the YOLOv8n network. Then, the SimAM attention mechanism is applied to the model feature extraction module, which enhances the multi-scale and spatial feature extraction capability of the model. Finally, MPDIoU is used as the boundary loss function to effectively solve the problem of low detection rate caused by the spatial overlap and occlusion of the lotus seed pods and lotus leaves.The results show that the improved FSM-YOLOv8 achieves 84.8%, 84.1%, and 87.9% of detection accuracy, 84.1%, and 87.9% of recall, respectively, compared with the YOLOv8n model, and reduces 13.4% of the parameter amount. 13.4%, which is a significant improvement in detection accuracy and model lightweighting, and can realize rapid identification of lotus seedpods in complex environments, and meet the demand of real-time identification of lotus seedpod picking robots in the process of picking.