Lightweight and Efficient Deep Learning Models for Fruit Detection in Orchards

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aut.relation.articlenumber26086
aut.relation.issue1
aut.relation.journalScientific Reports
aut.relation.volume14
dc.contributor.authorYang, Xiaoyao
dc.contributor.authorZhao, Wenyang
dc.contributor.authorWang, Yong
dc.contributor.authorYan, Wei Qi
dc.contributor.authorLi, Yanqiang
dc.date.accessioned2024-11-03T21:57:26Z
dc.date.available2024-11-03T21:57:26Z
dc.date.issued2024-10-30
dc.description.abstractThe accurate recognition of apples in complex orchard environments is a fundamental aspect of the operation of automated picking equipment. This paper aims to investigate the influence of dense targets, occlusion, and the natural environment in practical application scenarios. To this end, it constructs a fruit dataset containing different scenarios and proposes a real-time lightweight detection network, ELD(Efficient Lightweight object Detector). The EGSS(Efficient Ghost-shuffle Slim module) module and MCAttention(Mix channel Attention) are proposed as innovative solutions to the problems of feature extraction and classification. The attention mechanism is employed to construct a novel feature extraction network, which effectively utilizes the low-latitude feature information, significantly enhances the fine-grained feature information and gradient flow of the model, and improves the model’s anti-interference ability. Eliminate redundant channels with SlimPAN to further compress the network and optimise functionality. The network as a whole employs the Shape-IOU loss function, which considers the influence of the bounding box itself, thereby enhancing the robustness of the model. Finally, the target detection accuracy is enhanced through the transfer of knowledge from the teacher’s network through knowledge distillation, while ensuring that the overall network is sufficiently lightweight. The experimental results demonstrate that the ELD network, designed for fruit detection, achieves an accuracy of 87.4%. It has a relatively low number of parameters (4.3 x 10⁵), a GLOPs of only 1.7, and a high FPS of 156. This network can achieve high accuracy while consuming fewer computational resources and performing better than other networks.
dc.identifier.citationScientific Reports, ISSN: 2045-2322 (Online), Springer Science and Business Media LLC, 14(1). doi: 10.1038/s41598-024-76662-w
dc.identifier.doi10.1038/s41598-024-76662-w
dc.identifier.issn2045-2322
dc.identifier.urihttp://hdl.handle.net/10292/18221
dc.languageen
dc.publisherSpringer Science and Business Media LLC
dc.relation.urihttps://www.nature.com/articles/s41598-024-76662-w
dc.rightsOpen Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
dc.rights.accessrightsOpenAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleLightweight and Efficient Deep Learning Models for Fruit Detection in Orchards
dc.typeJournal Article
pubs.elements-id573804
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