Petrochemical Equipment Detection by Improved Yolov5 with Multiscale Deep Feature Fusion and Attention Mechanism
Petrochemical equipment detection technology plays important role in petrochemical industry security monitoring systems, equipment working status analysis systems, and other applications. In complex scenes, the accuracy and speed of petrochemical equipment detection would be limited because of the m...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
|
| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/8612174 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832552912635559936 |
|---|---|
| author | Zhenqiang Wei Shaohua Dong Xuchu Wang |
| author_facet | Zhenqiang Wei Shaohua Dong Xuchu Wang |
| author_sort | Zhenqiang Wei |
| collection | DOAJ |
| description | Petrochemical equipment detection technology plays important role in petrochemical industry security monitoring systems, equipment working status analysis systems, and other applications. In complex scenes, the accuracy and speed of petrochemical equipment detection would be limited because of the missing and false detection of equipment with extreme sizes, due to image quality, equipment scale, light, and other factors. In this paper, a one-stage attention mechanism-enhanced Yolov5 network is proposed to detect typical types of petrochemical equipment in industry scene images. The model considers the advantages of the channel and spatial attention mechanism and incorporates it into the three mainframes. Furthermore, the multiscale deep feature is fused with a bottom-up feature pyramid structure to learn the features of equipment with extreme sizes. Moreover, an adaptive anchor generation algorithm is proposed to handle objects with extreme sizes in a complex background. In addition, the data augmentation strategy is also introduced to handle the relatively small and extremely large sample and to enhance the robustness of the fused model. The proposed model was validated on the self-built petrochemical equipment image data set, and the experimental results show that it achieves a competitive performance in comparison with the related state-of-the-art detectors. |
| format | Article |
| id | doaj-art-55bb8aa05fef4ec99558b8d123689bb4 |
| institution | Kabale University |
| issn | 2090-0155 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-55bb8aa05fef4ec99558b8d123689bb42025-02-03T05:57:25ZengWileyJournal of Electrical and Computer Engineering2090-01552022-01-01202210.1155/2022/8612174Petrochemical Equipment Detection by Improved Yolov5 with Multiscale Deep Feature Fusion and Attention MechanismZhenqiang Wei0Shaohua Dong1Xuchu Wang2College of Safety and Ocean EngineeringCollege of Safety and Ocean EngineeringKey Laboratory of Optoelectronic Technology and Systems of Ministry of EducationPetrochemical equipment detection technology plays important role in petrochemical industry security monitoring systems, equipment working status analysis systems, and other applications. In complex scenes, the accuracy and speed of petrochemical equipment detection would be limited because of the missing and false detection of equipment with extreme sizes, due to image quality, equipment scale, light, and other factors. In this paper, a one-stage attention mechanism-enhanced Yolov5 network is proposed to detect typical types of petrochemical equipment in industry scene images. The model considers the advantages of the channel and spatial attention mechanism and incorporates it into the three mainframes. Furthermore, the multiscale deep feature is fused with a bottom-up feature pyramid structure to learn the features of equipment with extreme sizes. Moreover, an adaptive anchor generation algorithm is proposed to handle objects with extreme sizes in a complex background. In addition, the data augmentation strategy is also introduced to handle the relatively small and extremely large sample and to enhance the robustness of the fused model. The proposed model was validated on the self-built petrochemical equipment image data set, and the experimental results show that it achieves a competitive performance in comparison with the related state-of-the-art detectors.http://dx.doi.org/10.1155/2022/8612174 |
| spellingShingle | Zhenqiang Wei Shaohua Dong Xuchu Wang Petrochemical Equipment Detection by Improved Yolov5 with Multiscale Deep Feature Fusion and Attention Mechanism Journal of Electrical and Computer Engineering |
| title | Petrochemical Equipment Detection by Improved Yolov5 with Multiscale Deep Feature Fusion and Attention Mechanism |
| title_full | Petrochemical Equipment Detection by Improved Yolov5 with Multiscale Deep Feature Fusion and Attention Mechanism |
| title_fullStr | Petrochemical Equipment Detection by Improved Yolov5 with Multiscale Deep Feature Fusion and Attention Mechanism |
| title_full_unstemmed | Petrochemical Equipment Detection by Improved Yolov5 with Multiscale Deep Feature Fusion and Attention Mechanism |
| title_short | Petrochemical Equipment Detection by Improved Yolov5 with Multiscale Deep Feature Fusion and Attention Mechanism |
| title_sort | petrochemical equipment detection by improved yolov5 with multiscale deep feature fusion and attention mechanism |
| url | http://dx.doi.org/10.1155/2022/8612174 |
| work_keys_str_mv | AT zhenqiangwei petrochemicalequipmentdetectionbyimprovedyolov5withmultiscaledeepfeaturefusionandattentionmechanism AT shaohuadong petrochemicalequipmentdetectionbyimprovedyolov5withmultiscaledeepfeaturefusionandattentionmechanism AT xuchuwang petrochemicalequipmentdetectionbyimprovedyolov5withmultiscaledeepfeaturefusionandattentionmechanism |