Authors

Abstract

For accurate recognition of orange fruit targets, a detection algorithm based on YOLOv4 was applied in this research. The results showed that AP (average precision) of YOLOv4 had reached 98.17%, 2.14% and 2.67% respectively higher than SSD and Faster RCNN while recognition rate of traditional image processing algorithms was merely 54.94%. Additionally, the extent of occlusion was proved to have obvious influences on the accuracy of orange detection. The accuracy on slight occlusion conditions appeared to be higher than that on serious occlusion conditions. Generally, YOLOv4 detection algorithm showed its feasibility and superiority on fruit detection in the complex natural environment.

Abstract in Chinese

本研究采用了一种基于Yolov4的检测算法来准确识别橙果。结果表明，YOLOv4的检测平均精度达到了98.17%，分别比SSD和Faster RCNN提升了2.14%和2.67%，而传统图像处理算法的识别率仅为54.94%。此外，研究表明遮挡程度对橙果检测的精度有明显影响。处于轻度遮挡条件下的橙果检测精度高于重度遮挡条件下。总体而言，YOLOv4检测算法在复杂自然环境下的水果检测中表现出了可行性和优越性。