PG-YOLO: An efficient detection algorithm for pomegranate before fruit thinning

Abstract

The detection of pomegranate fruitlets is of great significance for fruit thinning and yield estimation. However, the researches on the detection of pomegranate fruitlets are rarely considered in recent years. In addition, the immature fruit detection algorithms often have a large amount of calculation, which require high computing memory and computing resources of the equipment. Based on the mentioned issue, we propose a lightweight detection method based on the improved You Only Look Once Version 8(YOLOv8) to realize the automatic detection of pomegranate fruitlets (PG-YOLO). Firstly, the lightweight network Shufflenetv2 is used to reconstruct the backbone of YOLOv8s. Secondly, the Depthwise separable convolution is used to replace the standard convolution of Neck layer. Then, the multi-head self-attention (MHSA) is used to enhance the positioning ability and feature extraction ability of the model before the backbone network outputs features. Finally, the self-built pomegranate fruitlet dataset is used to train and test the PG-YOLO model. It is shown that, the Mean Average Precision(mAP) of pomegranate fruitlets detected by PG-YOLO model is 93.4%. The optimal weight size of PG-YOLO is only 2.2 Mb, which is 89.9% less than the original YOLOv8s. In addition, the detection speed of the PG-YOLO model is 74.1% higher than the original YOLOv8s. The results demonstrate that the study significantly reduces the complexity of the model and provides a research basis for further application in mobile devices. The self-built PG-YOLO-Dataset used in this paper has been published on github: https://github.com/LforikC/PG-YOLO-Dataset.