Improved YOLOv4 recognition algorithm for pitaya based on coordinate attention and combinational convolution
Accurate recognition method of pitaya in natural environment provides technical support for automatic picking. Aiming at the intricate spatial position relationship between pitaya fruits and branches, a pitaya recognition method based on improved YOLOv4 was proposed. GhostNet feature extraction netw...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-10-01
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| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2022.1030021/full |
| _version_ | 1798003877374590976 |
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| author | Fu Zhang Fu Zhang Weihua Cao Shunqing Wang Xiahua Cui Ning Yang Xinyue Wang Xiaodong Zhang Sanling Fu |
| author_facet | Fu Zhang Fu Zhang Weihua Cao Shunqing Wang Xiahua Cui Ning Yang Xinyue Wang Xiaodong Zhang Sanling Fu |
| author_sort | Fu Zhang |
| collection | DOAJ |
| description | Accurate recognition method of pitaya in natural environment provides technical support for automatic picking. Aiming at the intricate spatial position relationship between pitaya fruits and branches, a pitaya recognition method based on improved YOLOv4 was proposed. GhostNet feature extraction network was used instead of CSPDarkNet53 as the backbone network of YOLOv4. A structure of generating a large number of feature maps through a small amount of calculation was used, and the redundant information in feature layer was obtained with lower computational cost, which can reduce the number of parameters and computation of the model. Coordinate attention was introduced to enhance the extraction of fine-grained feature of targets. An improved combinational convolution module was designed to save computing power and prevent the loss of effective features and improve the recognition accuracy. The Ghost Module was referenced in Yolo Head to improve computing speed and reduce delay. Precision, Recall, F1, AP, detection speed and weight size were selected as performance evaluation indexes of recognition model. 8800 images of pitaya fruit in different environments were used as the dataset, which were randomly divided into the training set, the validation set and the test set according to the ratio of 7:1:2. The research results show that the recognition accuracy of the improved YOLOv4 model for pitaya fruit is 99.23%. Recall, F1 and AP are 95.10%, 98% and 98.94%, respectively. The detection speed is 37.2 frames·s-1, and the weight size is 59.4MB. The improved YOLOv4 recognition algorithm can meet the requirements for the accuracy and the speed of pitaya fruit recognition in natural environment, which will ensure the rapid and accurate operation of the picking robot. |
| first_indexed | 2024-04-11T12:14:39Z |
| format | Article |
| id | doaj.art-178a256780ee4253b8c64bb047d1fa48 |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-04-11T12:14:39Z |
| publishDate | 2022-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-178a256780ee4253b8c64bb047d1fa482022-12-22T04:24:23ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2022-10-011310.3389/fpls.2022.10300211030021Improved YOLOv4 recognition algorithm for pitaya based on coordinate attention and combinational convolutionFu Zhang0Fu Zhang1Weihua Cao2Shunqing Wang3Xiahua Cui4Ning Yang5Xinyue Wang6Xiaodong Zhang7Sanling Fu8College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, ChinaCollaborative Innovation Center of Machinery Equipment Advanced Manufacturing of Henan Province, Henan University of Science and Technology, Luoyang, ChinaCollege of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, ChinaCollege of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, ChinaCollege of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, ChinaSchool of Electrical and Information Engineering, Jiangsu University, Zhenjiang, ChinaCollege of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, ChinaKey Laboratory of Modern Agricultural Equipment and Technology of Ministry of Education, Jiangsu University, Zhenjiang, ChinaCollege of Physical Engineering, Henan University of Science and Technology, Luoyang, ChinaAccurate recognition method of pitaya in natural environment provides technical support for automatic picking. Aiming at the intricate spatial position relationship between pitaya fruits and branches, a pitaya recognition method based on improved YOLOv4 was proposed. GhostNet feature extraction network was used instead of CSPDarkNet53 as the backbone network of YOLOv4. A structure of generating a large number of feature maps through a small amount of calculation was used, and the redundant information in feature layer was obtained with lower computational cost, which can reduce the number of parameters and computation of the model. Coordinate attention was introduced to enhance the extraction of fine-grained feature of targets. An improved combinational convolution module was designed to save computing power and prevent the loss of effective features and improve the recognition accuracy. The Ghost Module was referenced in Yolo Head to improve computing speed and reduce delay. Precision, Recall, F1, AP, detection speed and weight size were selected as performance evaluation indexes of recognition model. 8800 images of pitaya fruit in different environments were used as the dataset, which were randomly divided into the training set, the validation set and the test set according to the ratio of 7:1:2. The research results show that the recognition accuracy of the improved YOLOv4 model for pitaya fruit is 99.23%. Recall, F1 and AP are 95.10%, 98% and 98.94%, respectively. The detection speed is 37.2 frames·s-1, and the weight size is 59.4MB. The improved YOLOv4 recognition algorithm can meet the requirements for the accuracy and the speed of pitaya fruit recognition in natural environment, which will ensure the rapid and accurate operation of the picking robot.https://www.frontiersin.org/articles/10.3389/fpls.2022.1030021/fullimproved YOLOv4GhostNetcoordinate attentionimproved combinational convolution moduletarget recognition |
| spellingShingle | Fu Zhang Fu Zhang Weihua Cao Shunqing Wang Xiahua Cui Ning Yang Xinyue Wang Xiaodong Zhang Sanling Fu Improved YOLOv4 recognition algorithm for pitaya based on coordinate attention and combinational convolution Frontiers in Plant Science improved YOLOv4 GhostNet coordinate attention improved combinational convolution module target recognition |
| title | Improved YOLOv4 recognition algorithm for pitaya based on coordinate attention and combinational convolution |
| title_full | Improved YOLOv4 recognition algorithm for pitaya based on coordinate attention and combinational convolution |
| title_fullStr | Improved YOLOv4 recognition algorithm for pitaya based on coordinate attention and combinational convolution |
| title_full_unstemmed | Improved YOLOv4 recognition algorithm for pitaya based on coordinate attention and combinational convolution |
| title_short | Improved YOLOv4 recognition algorithm for pitaya based on coordinate attention and combinational convolution |
| title_sort | improved yolov4 recognition algorithm for pitaya based on coordinate attention and combinational convolution |
| topic | improved YOLOv4 GhostNet coordinate attention improved combinational convolution module target recognition |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2022.1030021/full |
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