Detection of grape cluster in stone hardening stage based on improved YOLOv5s

In order to realize the rapid and accurate detection of stone hardening stage grape clusters in the natural environment, an improved YOLOv5s network was proposed. Firstly, the convolutional module（Conv） in the backbone feature extraction network and the enhanced feature extraction network of YOLOv5s was replaced by the RepConv module with stronger feature extraction capabilities. Secondly, the BottleNeck in the C3 structure of the backbone feature extraction network of YOLOv5s was also replaced by the RepConv module. Thirdly, the Efficient Channel Attention（ECA） module was added to the C3structure of the enhanced feature extraction network of YOLOv5s. Finally, the activation function SiLU in the convolutional module of YOLOv5s was changed to ReLU6. The experimental results showed that the precision of the improved YOLOv5s network for grape cluster detection was 96. 5%, the recall rate was 94. 5%, the mean average precision was 98. 0%, and the detection speed was 260 f/s. Compared with Faster R-CNN, SSD, RetinaNet, YOLOv3（Ultralytics）, YOLOXs and YOLOv5s, the mean average precision of the improved YOLOv5s was 10. 4, 44. 1, 13. 9, 0. 2, 8. 9 and 1. 0 percentage points higher, respectively. The improved network proposed in this paper can effectively detect stone hardening stage grape clusters in various states, such as blur, occlusion, cluster adhesion, incompleteness, dimness, and backlight in the natural environment, and is easy to deploy on mobile devices.