手机知网 App
24小时专家级知识服务
打 开
园艺
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.
领 域:
园艺
格 式:
PDF原版;EPUB自适应版(需下载客户端)
0 52
手机阅读本文
下载APP 手机查看本文
Asian Agricultural Research
2022年01期
相似文献
图书推荐
相关工具书

搜 索