A Detection Method of Lentinus Edodes Based on Improved YOLOv4 Algorithm
In order to explore the picking of Lentinus edodes which are cultivated in bags, a recognition algorithm based on improved YOLOv4 is proposed.The main improvement measures are: in the structure of PANet (Path Aggregation Network), we add a feature map path with residual attention mechanism to improv...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Harbin University of Science and Technology Publications
2022-08-01
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| Series: | Journal of Harbin University of Science and Technology |
| Subjects: | |
| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2113 |
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| Summary: | In order to explore the picking of Lentinus edodes which are cultivated in bags, a recognition algorithm based on improved YOLOv4 is proposed.The main improvement measures are: in the structure of PANet (Path Aggregation Network), we add a feature map path with residual attention mechanism to improve the recognition accuracy of small targets, and replace the convolution layer in PANet network with deep separable convolution structure to reduce the amount of parameters.Focal loss is selected to improve the original confidence loss function.In the aspect of data preprocessing, gamma transform method is used to enhance and expand the data.In the training process, the idea of transfer learning is used to load the pre training weight of VOC data set on the backbone network.Compared with the original YOLOv4 algorithm, the mAP value is increased by 4.82 percentage points to 94.39%, and the amount of algorithm parameters is reduced by 58.13%.The algorithm is more efficient and lightweight, providing visual algorithm support for mechanical picking. |
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| ISSN: | 1007-2683 |