Open AccessImmature Apple Detection Method Based on Improved Yolov3
Author(s) -
Zhongqiang Huang,
Ping Zhang,
Ruigang Liu,
Dongxu Li
Publication year - 2021
Publication title -
asp transactions on internet of things
Language(s) - English
Resource type - Journals
ISSN - 2788-8401
DOI - 10.52810/tiot.2021.100028
Subject(s) - orchard , artificial intelligence , computer science , automation , set (abstract data type) , frame (networking) , computer vision , inference , data set , pattern recognition (psychology) , horticulture , engineering , mechanical engineering , telecommunications , biology , programming language
The identification of immature apples is a key technical link to realize automatic real-time monitoring of orchards, expert decision-making, and realization of orchard output prediction. In the orchard scene, the reflection caused by light and the color of immature apples are highly similar to the leaves, especially the obscuration and overlap of fruits by leaves and branches, which brings great challenges to the detection of immature apples. This paper proposes an improved YOLOv3 detection method for immature apples in the orchard scene. Use CSPDarknet53 as the backbone network of the model, introduce the CIOU target frame regression mechanism, and combine with the Mosaic algorithm to improve the detection accuracy. For the data set with severely occluded fruits, the F1 and mAP of the immature apple recognition model proposed in this article are 0.652 and 0.675, respectively. The inference speed for a single 416×416 picture is 12 ms, the detection speed can reach 83 frames/s on 1080ti, and the inference speed is 8.6 ms. Therefore, for the severely occluded immature apple data set, the method proposed in this article has a significant detection effect, and provides a feasible solution for the automation and mechanization of the apple industry.