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Lightweight and efficient neural network with SPSA attention for wheat ear detection

Advancements in deep neural networks have made remarkable leap-forwards in crop detection. However, the detection of wheat ears is an important yet challenging task due to the complex background, dense targets, and overlaps between wheat ears. Currently, many detectors have made significant progress...

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Autores principales: Dong, Yan, Liu, Yundong, Kang, Haonan, Li, Chunlei, Liu, Pengcheng, Liu, Zhoufeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9044259/
https://www.ncbi.nlm.nih.gov/pubmed/35494849
http://dx.doi.org/10.7717/peerj-cs.931
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author Dong, Yan
Liu, Yundong
Kang, Haonan
Li, Chunlei
Liu, Pengcheng
Liu, Zhoufeng
author_facet Dong, Yan
Liu, Yundong
Kang, Haonan
Li, Chunlei
Liu, Pengcheng
Liu, Zhoufeng
author_sort Dong, Yan
collection PubMed
description Advancements in deep neural networks have made remarkable leap-forwards in crop detection. However, the detection of wheat ears is an important yet challenging task due to the complex background, dense targets, and overlaps between wheat ears. Currently, many detectors have made significant progress in improving detection accuracy. However, some of them are not able to make a good balance between computational cost and precision to meet the needs of deployment in real world. To address these issues, a lightweight and efficient wheat ear detector with Shuffle Polarized Self-Attention (SPSA) is proposed in this paper. Specifically, we first utilize a lightweight backbone network with asymmetric convolution for effective feature extraction. Next, SPSA attention is given to adaptively select focused positions and produce a more discriminative representation of the features. This strategy introduces polarized self-attention to spatial dimension and channel dimension and adopts Shuffle Units to combine those two types of attention mechanisms effectively. Finally, the TanhExp activation function is adopted to accelerate the inference speed and reduce the training time, and CIOU loss is used as the border regression loss function to enhance the detection ability of occlusion and overlaps between targets. Experimental results on the Global Wheat Head Detection dataset show that our method achieves superior detection performance compared with other state-of-the-art approaches.
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spelling pubmed-90442592022-04-28 Lightweight and efficient neural network with SPSA attention for wheat ear detection Dong, Yan Liu, Yundong Kang, Haonan Li, Chunlei Liu, Pengcheng Liu, Zhoufeng PeerJ Comput Sci Artificial Intelligence Advancements in deep neural networks have made remarkable leap-forwards in crop detection. However, the detection of wheat ears is an important yet challenging task due to the complex background, dense targets, and overlaps between wheat ears. Currently, many detectors have made significant progress in improving detection accuracy. However, some of them are not able to make a good balance between computational cost and precision to meet the needs of deployment in real world. To address these issues, a lightweight and efficient wheat ear detector with Shuffle Polarized Self-Attention (SPSA) is proposed in this paper. Specifically, we first utilize a lightweight backbone network with asymmetric convolution for effective feature extraction. Next, SPSA attention is given to adaptively select focused positions and produce a more discriminative representation of the features. This strategy introduces polarized self-attention to spatial dimension and channel dimension and adopts Shuffle Units to combine those two types of attention mechanisms effectively. Finally, the TanhExp activation function is adopted to accelerate the inference speed and reduce the training time, and CIOU loss is used as the border regression loss function to enhance the detection ability of occlusion and overlaps between targets. Experimental results on the Global Wheat Head Detection dataset show that our method achieves superior detection performance compared with other state-of-the-art approaches. PeerJ Inc. 2022-04-05 /pmc/articles/PMC9044259/ /pubmed/35494849 http://dx.doi.org/10.7717/peerj-cs.931 Text en © 2022 Dong et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Artificial Intelligence
Dong, Yan
Liu, Yundong
Kang, Haonan
Li, Chunlei
Liu, Pengcheng
Liu, Zhoufeng
Lightweight and efficient neural network with SPSA attention for wheat ear detection
title Lightweight and efficient neural network with SPSA attention for wheat ear detection
title_full Lightweight and efficient neural network with SPSA attention for wheat ear detection
title_fullStr Lightweight and efficient neural network with SPSA attention for wheat ear detection
title_full_unstemmed Lightweight and efficient neural network with SPSA attention for wheat ear detection
title_short Lightweight and efficient neural network with SPSA attention for wheat ear detection
title_sort lightweight and efficient neural network with spsa attention for wheat ear detection
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9044259/
https://www.ncbi.nlm.nih.gov/pubmed/35494849
http://dx.doi.org/10.7717/peerj-cs.931
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