Tomato Young Fruits Detection Method under Near Color Background Based on Improved Faster R-CNN with Attention Mechanism

The information of tomato young fruits acquisition has an important impact on monitoring fruit growth, early control of pests and diseases and yield estimation. It is of great significance for timely removing young fruits with abnormal growth status, improving the fruits quality, and maintaining hig...

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Autores principales:	Peng Wang, Tong Niu, Dongjian He
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	convolutional neural network feature pyramid network near color background tomato young fruits fruit detection Agriculture (General) S1-972
Acceso en línea:	https://doaj.org/article/84bfa24c730f424683f3fda4ca037773
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id	oai:doaj.org-article:84bfa24c730f424683f3fda4ca037773
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spelling	oai:doaj.org-article:84bfa24c730f424683f3fda4ca0377732021-11-25T15:58:26ZTomato Young Fruits Detection Method under Near Color Background Based on Improved Faster R-CNN with Attention Mechanism10.3390/agriculture111110592077-0472https://doaj.org/article/84bfa24c730f424683f3fda4ca0377732021-10-01T00:00:00Zhttps://www.mdpi.com/2077-0472/11/11/1059https://doaj.org/toc/2077-0472The information of tomato young fruits acquisition has an important impact on monitoring fruit growth, early control of pests and diseases and yield estimation. It is of great significance for timely removing young fruits with abnormal growth status, improving the fruits quality, and maintaining high and stable yields. Tomato young fruits are similar in color to the stems and leaves, and there are interference factors, such as fruits overlap, stems and leaves occlusion, and light influence. In order to improve the detection accuracy and efficiency of tomato young fruits, this paper proposes a method for detecting tomato young fruits with near color background based on improved Faster R-CNN with an attention mechanism. First, ResNet50 is used as the feature extraction backbone, and the feature map extracted is optimized through Convolutional Block Attention Module (CBAM). Then, Feature Pyramid Network (FPN) is used to integrate high-level semantic features into low-level detailed features to enhance the model sensitivity of scale. Finally, Soft Non-Maximum Suppression (Soft-NMS) is used to reduce the missed detection rate of overlapping fruits. The results show that the mean Average Precision (mAP) of the proposed method reaches 98.46%, and the average detection time per image is only 0.084 s, which can achieve the real-time and accurate detection of tomato young fruits. The research shows that the method in this paper can efficiently identify tomato young fruits, and provides a better solution for the detection of fruits with near color background.Peng WangTong NiuDongjian HeMDPI AGarticleconvolutional neural networkfeature pyramid networknear color backgroundtomato young fruitsfruit detectionAgriculture (General)S1-972ENAgriculture, Vol 11, Iss 1059, p 1059 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	convolutional neural network feature pyramid network near color background tomato young fruits fruit detection Agriculture (General) S1-972
spellingShingle	convolutional neural network feature pyramid network near color background tomato young fruits fruit detection Agriculture (General) S1-972 Peng Wang Tong Niu Dongjian He Tomato Young Fruits Detection Method under Near Color Background Based on Improved Faster R-CNN with Attention Mechanism
description	The information of tomato young fruits acquisition has an important impact on monitoring fruit growth, early control of pests and diseases and yield estimation. It is of great significance for timely removing young fruits with abnormal growth status, improving the fruits quality, and maintaining high and stable yields. Tomato young fruits are similar in color to the stems and leaves, and there are interference factors, such as fruits overlap, stems and leaves occlusion, and light influence. In order to improve the detection accuracy and efficiency of tomato young fruits, this paper proposes a method for detecting tomato young fruits with near color background based on improved Faster R-CNN with an attention mechanism. First, ResNet50 is used as the feature extraction backbone, and the feature map extracted is optimized through Convolutional Block Attention Module (CBAM). Then, Feature Pyramid Network (FPN) is used to integrate high-level semantic features into low-level detailed features to enhance the model sensitivity of scale. Finally, Soft Non-Maximum Suppression (Soft-NMS) is used to reduce the missed detection rate of overlapping fruits. The results show that the mean Average Precision (mAP) of the proposed method reaches 98.46%, and the average detection time per image is only 0.084 s, which can achieve the real-time and accurate detection of tomato young fruits. The research shows that the method in this paper can efficiently identify tomato young fruits, and provides a better solution for the detection of fruits with near color background.
format	article
author	Peng Wang Tong Niu Dongjian He
author_facet	Peng Wang Tong Niu Dongjian He
author_sort	Peng Wang
title	Tomato Young Fruits Detection Method under Near Color Background Based on Improved Faster R-CNN with Attention Mechanism
title_short	Tomato Young Fruits Detection Method under Near Color Background Based on Improved Faster R-CNN with Attention Mechanism
title_full	Tomato Young Fruits Detection Method under Near Color Background Based on Improved Faster R-CNN with Attention Mechanism
title_fullStr	Tomato Young Fruits Detection Method under Near Color Background Based on Improved Faster R-CNN with Attention Mechanism
title_full_unstemmed	Tomato Young Fruits Detection Method under Near Color Background Based on Improved Faster R-CNN with Attention Mechanism
title_sort	tomato young fruits detection method under near color background based on improved faster r-cnn with attention mechanism
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/84bfa24c730f424683f3fda4ca037773
work_keys_str_mv	AT pengwang tomatoyoungfruitsdetectionmethodundernearcolorbackgroundbasedonimprovedfasterrcnnwithattentionmechanism AT tongniu tomatoyoungfruitsdetectionmethodundernearcolorbackgroundbasedonimprovedfasterrcnnwithattentionmechanism AT dongjianhe tomatoyoungfruitsdetectionmethodundernearcolorbackgroundbasedonimprovedfasterrcnnwithattentionmechanism
_version_	1718413389741424640

Tomato Young Fruits Detection Method under Near Color Background Based on Improved Faster R-CNN with Attention Mechanism

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