An Improved YOLOv3-Based Method for Immature Apple Detection

Zhongqiang Huang; Ping Zhang; Ruigang Liu; Dongxu Li

doi:10.62762/TIOT.2023.539452

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Volume 1, Issue 1, IECE Transactions on Internet of Things

Volume 1, Issue 1, 2023

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Jinchao Chen

Northwestern Polytechnical University,China

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IECE Transactions on Internet of Things, 2023, Volume 1, Issue 1: 9-14

Free to Read | Research Article | Feature Paper | 12 June 2023

An Improved YOLOv3-Based Method for Immature Apple Detection

Zhongqiang Huang 1

Ping Zhang 2 *

Ruigang Liu 1

Dongxu Li 2

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]

DOI: https://doi.org/10.62762/TIOT.2023.539452

Received: 03 May 2023, Accepted: 09 June 2023, Published: 12 June 2023

Cited by: 13 (Source: Web of Science) , 16 (Source: Google Scholar)

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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

An Improved YOLOv3-Based Method for Immature Apple Detection

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.

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