An Improved YOLOv8 Model for Lotus Seedpod Instance Segmentation in the Lotus Pond Environment

摘要

Lotus seedpod maturity detection and segmentation in pond environments play a significant role in yield prediction and picking pose estimation for lotus seedpods. However, it is a great challenge to accurately detect and segment lotus seedpods due to insignificant phenotypic differences between the adjacent maturity, changing illumination, overlap, and occlusion of lotus seedpods. The existing research pays attention to lotus seedpod detection while ignoring maturity detection and segmentation problems. Therefore, a semantic segmentation dataset of lotus seedpods was created, where a copy-and-paste data augmentation tool was employed to eliminate the class-imbalanced problem and improve model generalization ability. Afterwards, an improved YOLOv8-seg model was proposed to detect and segment the maturity of lotus seedpods. In the model, the convolutional block attention module (CBAM) was embedded in the neck network to extract distinguished features of different maturity stages with negligible computation cost. Wise-Intersection over Union (WIoU) regression loss function was adopted to refine the regression inference bias and improve the bounding box prediction accuracy. The experimental results showed that the proposed YOLOv8-seg model provides an effective method for “ripe” and “overripe” lotus seedpod detection and instance segmentation, where the mean average precision of segmentation mask (mAPmask) reaches 97.4% and 98.6%, respectively. In addition, the improved YOLOv8-seg exhibits high robustness and adaptability to complex illumination in a challenging environment. Comparative experiments were conducted using the proposed YOLOv8-seg and other state-of-the-art instance segmentation methods. The results showed that the improved model is superior to the Mask R-CNN and YOLACT models, with recall, precision, mAPbox and mAPmask being 96.5%, 94.3%, 97.8%, and 98%, respectively. The average running time and weight size of the proposed model are 25.9 ms and 7.4 M, respectively. The proposed model obtained the highest mAP for lotus seedpod maturity detection and segmentation while maintaining an appropriate model size and speed. Furthermore, based on the obtained segmentation model, 3D visualization of the lotus pond scene is performed, and cloud point of lotus seedpods is generated, which provides a theoretical foundation for robot harvesting in the lotus pond.