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An Improved YOLOv3-Based Method for Immature Apple Detection
1
School of Informatics, Huazhong Agricultural University, Wuhan 430070, China
2
School of Computer, BaoJi University of Arts and Sciences, Baoji 721016, China
Corresponding Author:
Ping Zhang,
[email protected]
Volume 1, Issue 1
2023
Received: 03 May 2023
Accepted: 09 June 2023
Published: 12 June 2023
Article Information
Published in
ICCK Transactions on Internet of Things
Volume/Issue
Volume 1, Issue 1, 2023
Pages
9-14
Abstract
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.
Graphical Abstract
Keywords
Orchard scene
Immature apple
Improved YOLOv3
Mosaic algorithm
CIOU target frame regression mechanism
Funding
This research was funded by the Special Projectof Education Department of Shaanxi ProvincialGovernment of china, grant number 16JK1048.
References
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TY - JOUR AU - Huang, Zhongqiang AU - Zhang, Ping AU - Liu, Ruigang AU - Li, Dongxu PY - 2023 DA - 2023/06/12 TI - An Improved YOLOv3-Based Method for Immature Apple Detection JO - ICCK Transactions on Internet of Things T2 - ICCK Transactions on Internet of Things JF - ICCK Transactions on Internet of Things VL - 1 IS - 1 SP - 9 EP - 14 DO - 10.62762/TIOT.2023.539452 UR - https://www.icck.org/article/abs/TIOT.2023.539452 KW - Orchard scene KW - Immature apple KW - Improved YOLOv3 KW - Mosaic algorithm KW - CIOU target frame regression mechanism AB - 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. SN - pending PB - Institute of Central Computation and Knowledge LA - English ER -
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@article{Huang2023An,
author = {Zhongqiang Huang and Ping Zhang and Ruigang Liu and Dongxu Li},
title = {An Improved YOLOv3-Based Method for Immature Apple Detection},
journal = {ICCK Transactions on Internet of Things},
year = {2023},
volume = {1},
number = {1},
pages = {9-14},
doi = {10.62762/TIOT.2023.539452},
url = {https://www.icck.org/article/abs/TIOT.2023.539452},
abstract = {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.},
keywords = {Orchard scene, Immature apple, Improved YOLOv3, Mosaic algorithm, CIOU target frame regression mechanism},
issn = {pending},
publisher = {Institute of Central Computation and Knowledge}
}
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