[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.