Detection Method for Sweet Cherry Fruits Based on YOLOv4 in the Natural Environment

[Objectives]To explore a rapid detection method of sweet cherry fruits in natural environment.[Methods]The cutting-edge YOLOv4 deep learning model was used.The YOLOv4 detection model was built on the CSP Darknet5 framework.A mosaic data enhancement method was used to expand the image dataset,and the model was processed to facilitate the detection of three different occlusion situations:no occlusion,branch and leaf occlusion,and fruit overlap occlusion,and the detection of sweet cherry fruits with different fruit numbers.[Results]In the three occlusion cases,the mean average precision(mAP)of the YOLOv4 algorithm was 95.40%,95.23%,and 92.73%,respectively.Different numbers of sweet cherry fruits were detected and identified,and the average value of mAP was 81.00%.To verify the detection performance of the YOLOv4 model for sweet cherry fruits,the model was compared with YOLOv3,SSD,and Faster-RCNN.The mAP of the YOLOv4 model was 90.89%and the detection speed was 22.86 f/s.The mAP was 0.66%,1.97%,and 12.46%higher than those of the other three algorithms.The detection speed met the actual production needs.[Conclusions]The YOLOv4 model is valuable for picking and identifying sweet cherry fruits.