The study of recognizing ripe strawberries based on the improved YOLOv7-Tiny model

Abstract

In image recognition, the overlap of strawberries seriously reduces the recognition efficiency of ripe strawberries. This paper proposes an improved YOLOv7-Tiny model for recognizing ripe strawberries. A lightweight RepGhost model is added to the YOLOv7-Tiny model to reduce the computation and the number of model parameters. The SiLU function replaces the LeakeyReLU activation function of the backbone CBL conditional block to improve the nonlinear fitting and feature learning capabilities of the mode. The nonlinear fitting and feature learning capabilities of the model are improved. The C3 module is fused in the small-object layer to improve the ability to extract information from small objects. The performance of the improved YOLOv7-Tiny model is validated through experiments. The results show that the parameters of the model are reduced by 26.9%, the calculation amount is reduced by 55.4%, the recognition speed is improved by 26.3%, and the mean average precision (mAP) is 89.8%. Compared with SSD, Faster RCNN, YOLOv3, YOLOv4, and YOLOv5s models, the mAP of the YOLOv7-Tiny model increased by 14.2%, 1.52%, 3.15%, 3.01%, and 2.6%. The recognition speed increased by 79.3%, 92.9%, 80.4%, 58.8%, and 69.6%. The number of parameters decreased by 90%, 89.7%, 95%, 47.8%, and 14.6%. The recognition accuracy of overlapping and small strawberries is significantly improved in the improved YOLOv7-Tiny model. The model provides technical support for efficient automatic strawberry picking.