Lightweight Detection Method for Real-Time Monitoring Tomato Growth Based on Improved YOLOv5s
In order to monitor the growth and development of tomatoes, and improve the efficiency of flower and fruit thinning and tomato picking, this paper constructs a tomato flower and fruit dataset and proposes a TF-YOLOv5s model for the detection of tomato flowers and fruits in natural environments. Base...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10443476/ |
| _version_ | 1797289829934825472 |
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| author | Suyu Tian Chao Fang Xiaogang Zheng Jue Liu |
| author_facet | Suyu Tian Chao Fang Xiaogang Zheng Jue Liu |
| author_sort | Suyu Tian |
| collection | DOAJ |
| description | In order to monitor the growth and development of tomatoes, and improve the efficiency of flower and fruit thinning and tomato picking, this paper constructs a tomato flower and fruit dataset and proposes a TF-YOLOv5s model for the detection of tomato flowers and fruits in natural environments. Based on the YOLOv5s model, a C3Faster module is introduced to reduce the number of parameters and calculations while maintaining detection accuracy. The regular convolution is replaced by depth-wise separable convolution (DWConv) to avoid parameter redundancy. To improve the convergence and accuracy of the model, this paper replaces Complete Intersection over Union (CIoU) loss with Efficient Intersection over Union (EIoU) loss. The Squeeze-and-Excitation (SE) module is added to improve the model’s sensitivity to the features of the tomato flowers and fruits. Compared with the baseline model, the number of parameters is reduced by 54.5%, the weight file is reduced by 52.8%, the Floating-point Operation Per second (FLOPs) is reduced by 48.7%. The detection accuracy of tomato flowers and fruits mAP@0.5 has improved by 1.4% and 1.2% respectively. TF-YOLOv5s is used to detect three types of targets: tomato flowers, red tomatoes, and green tomatoes, and mAP@0.5 of which can reach as high as 95.2%. Furthermore, the improved algorithm is deployed on two edge computing devices to verify its effectiveness. Experimental results show that the algorithm in this paper can achieve high detection with less computational resources. This algorithm has the potential value of application in practical tomato production. |
| first_indexed | 2024-03-07T19:11:40Z |
| format | Article |
| id | doaj.art-cb5dfdcb993c4441b25e1333c028f482 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-07T19:11:40Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-cb5dfdcb993c4441b25e1333c028f4822024-03-01T00:00:49ZengIEEEIEEE Access2169-35362024-01-0112298912989910.1109/ACCESS.2024.336891410443476Lightweight Detection Method for Real-Time Monitoring Tomato Growth Based on Improved YOLOv5sSuyu Tian0https://orcid.org/0009-0000-9025-3789Chao Fang1https://orcid.org/0000-0003-3209-7233Xiaogang Zheng2Jue Liu3School of Electrical and Electronic Engineering, Wuhan Polytechnic University, Wuhan, ChinaSchool of Electrical and Electronic Engineering, Wuhan Polytechnic University, Wuhan, ChinaSchool of Electrical and Electronic Engineering, Wuhan Polytechnic University, Wuhan, ChinaSchool of Electrical and Electronic Engineering, Wuhan Polytechnic University, Wuhan, ChinaIn order to monitor the growth and development of tomatoes, and improve the efficiency of flower and fruit thinning and tomato picking, this paper constructs a tomato flower and fruit dataset and proposes a TF-YOLOv5s model for the detection of tomato flowers and fruits in natural environments. Based on the YOLOv5s model, a C3Faster module is introduced to reduce the number of parameters and calculations while maintaining detection accuracy. The regular convolution is replaced by depth-wise separable convolution (DWConv) to avoid parameter redundancy. To improve the convergence and accuracy of the model, this paper replaces Complete Intersection over Union (CIoU) loss with Efficient Intersection over Union (EIoU) loss. The Squeeze-and-Excitation (SE) module is added to improve the model’s sensitivity to the features of the tomato flowers and fruits. Compared with the baseline model, the number of parameters is reduced by 54.5%, the weight file is reduced by 52.8%, the Floating-point Operation Per second (FLOPs) is reduced by 48.7%. The detection accuracy of tomato flowers and fruits mAP@0.5 has improved by 1.4% and 1.2% respectively. TF-YOLOv5s is used to detect three types of targets: tomato flowers, red tomatoes, and green tomatoes, and mAP@0.5 of which can reach as high as 95.2%. Furthermore, the improved algorithm is deployed on two edge computing devices to verify its effectiveness. Experimental results show that the algorithm in this paper can achieve high detection with less computational resources. This algorithm has the potential value of application in practical tomato production.https://ieeexplore.ieee.org/document/10443476/Tomato flower fruit recognitionC3Fasterconvolutional neural networkslightweight |
| spellingShingle | Suyu Tian Chao Fang Xiaogang Zheng Jue Liu Lightweight Detection Method for Real-Time Monitoring Tomato Growth Based on Improved YOLOv5s IEEE Access Tomato flower fruit recognition C3Faster convolutional neural networks lightweight |
| title | Lightweight Detection Method for Real-Time Monitoring Tomato Growth Based on Improved YOLOv5s |
| title_full | Lightweight Detection Method for Real-Time Monitoring Tomato Growth Based on Improved YOLOv5s |
| title_fullStr | Lightweight Detection Method for Real-Time Monitoring Tomato Growth Based on Improved YOLOv5s |
| title_full_unstemmed | Lightweight Detection Method for Real-Time Monitoring Tomato Growth Based on Improved YOLOv5s |
| title_short | Lightweight Detection Method for Real-Time Monitoring Tomato Growth Based on Improved YOLOv5s |
| title_sort | lightweight detection method for real time monitoring tomato growth based on improved yolov5s |
| topic | Tomato flower fruit recognition C3Faster convolutional neural networks lightweight |
| url | https://ieeexplore.ieee.org/document/10443476/ |
| work_keys_str_mv | AT suyutian lightweightdetectionmethodforrealtimemonitoringtomatogrowthbasedonimprovedyolov5s AT chaofang lightweightdetectionmethodforrealtimemonitoringtomatogrowthbasedonimprovedyolov5s AT xiaogangzheng lightweightdetectionmethodforrealtimemonitoringtomatogrowthbasedonimprovedyolov5s AT jueliu lightweightdetectionmethodforrealtimemonitoringtomatogrowthbasedonimprovedyolov5s |