Intelligent Detection of Lightweight “Yuluxiang” Pear in Non-Structural Environment Based on YOLO-GEW

Abstract

To detect quickly and accurately “Yuluxiang” pear fruits in non-structural environments, a lightweight YOLO-GEW detection model is proposed to address issues such as similar fruit color to leaves, fruit bagging, and complex environments. This model improves upon YOLOv8s by using GhostNet as its backbone for extracting features of the “Yuluxiang” pears. Additionally, an EMA attention mechanism was added before fusing each feature in the neck section to make the model focus more on the target information of “Yuluxiang” pear fruits, thereby improving target recognition ability and localization accuracy. Furthermore, the CIoU Loss was replaced with the WIoUv3 Loss as the loss function, which enhances the capability of bounding box fitting and improves model performance without increasing its size. Experimental results demonstrated that the enhanced YOLO-GEW achieves an F1 score of 84.47% and an AP of 88.83%, while only occupying 65.50% of the size of YOLOv8s. Compared to lightweight algorithms such as YOLOv8s, YOLOv7-Tiny, YOLOv6s, YOLOv5s, YOLOv4-Tiny, and YOLOv3-Tiny; there are improvements in AP by 2.32%, 1.51%, 2.95%, 2.06%, 2.92%, and 5.38% respectively. This improved model can efficiently detect “Yuluxiang” pears in non-structural environments in real-time and provides a theoretical basis for recognition systems used by picking robots.