Wood Surface Defects Detection Based on the Improved YOLOv5-C3Ghost With SimAm Module
The detection of defects in wood is valuable for promoting the efficient exploitation of wood. So it is significant to further increase the accuracy of the detection of wood defects and enhance the real-time detection. In this paper, the YOLOv5 convolutional neural network is applied to wood defects...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10214278/ |
| _version_ | 1797663949400834048 |
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| author | Jipan Xu Hong Yang Zihao Wan Hongbo Mu Dawei Qi Shuxia Han |
| author_facet | Jipan Xu Hong Yang Zihao Wan Hongbo Mu Dawei Qi Shuxia Han |
| author_sort | Jipan Xu |
| collection | DOAJ |
| description | The detection of defects in wood is valuable for promoting the efficient exploitation of wood. So it is significant to further increase the accuracy of the detection of wood defects and enhance the real-time detection. In this paper, the YOLOv5 convolutional neural network is applied to wood defects detection, and the model is modified for both the YOLOv5n and YOLOv5m scales. The SimAM attention model was first incorporated into the network, and the learning rate decay strategy was replaced with CosLR, with Ghost convolution employed to minimize the model parameters. Finally, the modified network was tested for five types of wood defects, including live-knot, resin, dead-knot, knot-with-crack, and crack. It is demonstrated that the improvements resulted in a 1.5% increase in mAP0.5:0.95 for YOLOv5n-C3Ghost and a 1.6% increase in mAP0.5:0.95 for YOLOv5m-C3Ghost. In addition, there is a 51% and 63% difference in the number of model parameters, and a decrease in inference time and floating point operations respectively. The experiments indicate that our improved method not only enhances the accuracy of YOLOv5 in detecting wood defects, but also enables a reduction in the volume and computational cost of the model parameters. |
| first_indexed | 2024-03-11T19:22:13Z |
| format | Article |
| id | doaj.art-88bf7c362dc247569d5158ba69760b39 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T19:22:13Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-88bf7c362dc247569d5158ba69760b392023-10-06T23:00:23ZengIEEEIEEE Access2169-35362023-01-011110528110528710.1109/ACCESS.2023.330389010214278Wood Surface Defects Detection Based on the Improved YOLOv5-C3Ghost With SimAm ModuleJipan Xu0https://orcid.org/0009-0009-2894-8761Hong Yang1Zihao Wan2Hongbo Mu3Dawei Qi4Shuxia Han5College of Science, Northeast Forestry University, Harbin, ChinaCollege of Science, Northeast Forestry University, Harbin, ChinaCollege of Science, Northeast Forestry University, Harbin, ChinaCollege of Science, Northeast Forestry University, Harbin, ChinaCollege of Science, Northeast Forestry University, Harbin, ChinaCollege of Science, Northeast Forestry University, Harbin, ChinaThe detection of defects in wood is valuable for promoting the efficient exploitation of wood. So it is significant to further increase the accuracy of the detection of wood defects and enhance the real-time detection. In this paper, the YOLOv5 convolutional neural network is applied to wood defects detection, and the model is modified for both the YOLOv5n and YOLOv5m scales. The SimAM attention model was first incorporated into the network, and the learning rate decay strategy was replaced with CosLR, with Ghost convolution employed to minimize the model parameters. Finally, the modified network was tested for five types of wood defects, including live-knot, resin, dead-knot, knot-with-crack, and crack. It is demonstrated that the improvements resulted in a 1.5% increase in mAP0.5:0.95 for YOLOv5n-C3Ghost and a 1.6% increase in mAP0.5:0.95 for YOLOv5m-C3Ghost. In addition, there is a 51% and 63% difference in the number of model parameters, and a decrease in inference time and floating point operations respectively. The experiments indicate that our improved method not only enhances the accuracy of YOLOv5 in detecting wood defects, but also enables a reduction in the volume and computational cost of the model parameters.https://ieeexplore.ieee.org/document/10214278/Wood defects detectionconvolutional neural networksYOLOv5SimAM |
| spellingShingle | Jipan Xu Hong Yang Zihao Wan Hongbo Mu Dawei Qi Shuxia Han Wood Surface Defects Detection Based on the Improved YOLOv5-C3Ghost With SimAm Module IEEE Access Wood defects detection convolutional neural networks YOLOv5 SimAM |
| title | Wood Surface Defects Detection Based on the Improved YOLOv5-C3Ghost With SimAm Module |
| title_full | Wood Surface Defects Detection Based on the Improved YOLOv5-C3Ghost With SimAm Module |
| title_fullStr | Wood Surface Defects Detection Based on the Improved YOLOv5-C3Ghost With SimAm Module |
| title_full_unstemmed | Wood Surface Defects Detection Based on the Improved YOLOv5-C3Ghost With SimAm Module |
| title_short | Wood Surface Defects Detection Based on the Improved YOLOv5-C3Ghost With SimAm Module |
| title_sort | wood surface defects detection based on the improved yolov5 c3ghost with simam module |
| topic | Wood defects detection convolutional neural networks YOLOv5 SimAM |
| url | https://ieeexplore.ieee.org/document/10214278/ |
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