GHD Net: Overlapping Green Tomato Instance Segmentation Under Facility Environments

This article proposes GhostConv-Dynamic Snake Convolution Network (GHD Net), a lightweight segmentation model designed to accurately recognize overlapping green tomatoes in complex facility environments. It is optimized using the YOLOv8-n algorithm. GHD Net efficiently recognizes and segments green fruit images affected by overlapping occlusions, varying illumination conditions, and capture angles. Specifically, the model enhances the feature extraction network by introducing a lightweight convolutional technique, GhostConv, to extract features more efficiently while maintaining high performance. In the feature fusion stage, the dynamic snake convolution module adaptively adjusts its position based on the shape and boundary information of the target, accurately capturing target edges and enhancing the representation of overlapping and occluded edge features. The classification and regression subnetworks predict fruit categories and bounding boxes, respectively, while the segmentation subnetwork generates mask prototypes and mask coefficients through Proto branches, ensuring the accuracy and consistency of the segmentation process. The GHD Net model has a parameter count of 3.242 M and a computational complexity of 12.0 GFLOPs, achieving average accuracies of 79.7% and 73.9% in green tomato detection and segmentation tasks, respectively. GHD Net outperforms existing models in terms of accuracy and computational complexity, meeting the accuracy and efficiency requirements for green fruit recognition in smart agricultural equipment.