FCOS-EAM: An accurate segmentation method for overlapping green fruits

Jia, Weikuan, Cao, Kai, Liu, Mengyuan, Lu, Yuqi, Ji, Ze ORCID: https://orcid.org/0000-0002-8968-9902, Liu, Guoliang, Yin, Xiang and Ge, Xinting 2024. FCOS-EAM: An accurate segmentation method for overlapping green fruits. Computers and Electronics in Agriculture 226 , 109392. 10.1016/j.compag.2024.109392 Item availability restricted.

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Abstract

Accurate fruit detection and segmentation based on deep learning is the key to successful harvesting robot operations, but the complex background of orchards, light and branch shading, and fruit overlap lead to low detection and segmentation accuracy and high complexity of existing methods. To address these problems, an improved green fruit segmentation method based on FCOS is proposed in this study. Firstly, its segmentation function for green fruits is realized by adding segmentation module. Then, the FCOS head network is improved by adding the Border-attention module (BAM) to detect the boundary of green fruits with higher accuracy. In addition, the features of mask branch and edge segmentation branch are fused in the segmentation module, and the appearance commonality is learned by modeling the pairwise affinity between all pixels of the feature map using non-local affinity-parsing, and finally the segmentation prediction results are output by combining the feature map of fruit shape and appearance commonality. The experimental results show that this model achieves 81.2% segmentation accuracy on apple dataset and 77.9% segmentation accuracy on persimmon dataset with relatively low guarantee complexity, which exceeds most current segmentation models. Meanwhile, this model has high robustness and can be used for the detection and segmentation work of other green fruits and vegetables in orchards, while effectively extending the application of agricultural equipment.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Elsevier
ISSN:	0168-1699
Date of First Compliant Deposit:	6 November 2024
Date of Acceptance:	25 August 2024
Last Modified:	06 Nov 2024 10:00
URI:	https://orca.cardiff.ac.uk/id/eprint/172572

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