Peach ripeness classification based on a new one-stage instance segmentation model
Ziang Zhao, Yulia Hicks, Xianfang Sun, Chaoxi Luo
Abstract
Peach instance segmentation is a crucial part to locate peaches and classify their ripeness stages to build an automatic peach harvesting or monitoring machine. This paper proposes a large and high-quality peach dataset called NinePeach, and a new one-stage instance segmentation model. The NinePeach dataset aims to reproduce real-world field conditions, encompassing various factors that can significantly influence the accuracy of peach detection, such as varying natural light intensity, instances of multiple fruit adhesion, and occlusion caused by stems and leaves. This is the largest and the most varied peach dataset among publicly available peach datasets to our best knowledge. Our proposed one-stage segmentation model does not require Region Proposal Network (RPN) to generate bounding box proposals, it directly identifies object instances by their centre locations and sizes and predict their category at the same time. The proposed model incorporates channel attention and spatial attention mechanisms to enhance object detection capabilities in crucial channels and spatial locations. Experimental results show that the state-of-the-art Mask RCNN performs 69.91% average precision (AP) with Swin-T backbone, our model surpasses it with the same backbone, achieving the highest 72.12% AP, and delivering more precise mask and boundary predictions. Specifically, our model is capable of accurately detect peaches under various conditions, such as peaches partially obscured by leaves, peaches partially exposed or overlapped. These advancements present promising prospects for the application of this technology to other fruits or crops.