PHAM-YOLO: A Parallel Hybrid Attention Mechanism Network for Defect Detection of Meter in Substation
Accurate detection and timely treatment of component defects in substations is an important measure to ensure the safe operation of power systems. In this study, taking substation meters as an example, a dataset of common meter defects, such as a fuzzy or damaged dial on the meter and broken meter h...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/13/6052 |
| _version_ | 1797436081880170496 |
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| author | Hao Dong Mu Yuan Shu Wang Long Zhang Wenxia Bao Yong Liu Qingyuan Hu |
| author_facet | Hao Dong Mu Yuan Shu Wang Long Zhang Wenxia Bao Yong Liu Qingyuan Hu |
| author_sort | Hao Dong |
| collection | DOAJ |
| description | Accurate detection and timely treatment of component defects in substations is an important measure to ensure the safe operation of power systems. In this study, taking substation meters as an example, a dataset of common meter defects, such as a fuzzy or damaged dial on the meter and broken meter housing, is constructed from the images of manual inspection in power systems. There are several challenges involved in accurately detecting defects in substation meter images, such as the complex background, different meter sizes and large differences in the shapes of meter defects. Therefore, this paper proposes the PHAM-YOLO (Parallel Hybrid Attention Mechanism You Only Look Once) network for automatic detection of substation meter defects. In order to make the network pay attention to the key areas against the complex background of the meter defect images and the differences between different defect features, a Parallel Hybrid Attention Mechanism (PHAM) module is designed and added to the backbone of YOLOv5. PHAM integration of local and non-local correlation information can highlight these differences while remaining focused on the meter defect features. To improve the expressive ability of the feature map, a Spatial Pyramid Pooling Fast (SPPF) module is introduced, which pools the input feature map using a continuous fixed convolution kernel, fusing the feature maps of different receptive fields. Bounding box regression (BBR) is the key way to determine object positioning performance in defect detection. EIOU (Efficient Intersection over Union) is, therefore, introduced as a boundary loss function to solve the ambiguity of the CIOU (Complete Intersection Over Union) loss function, making the BBR regression more accurate. The experimental results show that the Average Precision Mean (mAP), Precision (P) and Recall (R) of the proposed PHAM-YOLO network in the dataset are 78.3%, 78.3%, and 79.9%, respectively, with mAP being improved by 2.7% compared to the original model and higher than SSD, Fast R-CNN, etc. |
| first_indexed | 2024-03-09T10:57:33Z |
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| institution | Directory Open Access Journal |
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| language | English |
| last_indexed | 2024-03-09T10:57:33Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b63fbca13ea044d9a6c4cb7155df48772023-12-01T01:34:09ZengMDPI AGSensors1424-82202023-06-012313605210.3390/s23136052PHAM-YOLO: A Parallel Hybrid Attention Mechanism Network for Defect Detection of Meter in SubstationHao Dong0Mu Yuan1Shu Wang2Long Zhang3Wenxia Bao4Yong Liu5Qingyuan Hu6Science Island Branch of Graduate School, University of Science and Technology of China, Hefei 230031, ChinaSchool of Electronics and Information Engineering, Anhui University, Hefei 230601, ChinaHefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, ChinaHefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, ChinaSchool of Electronics and Information Engineering, Anhui University, Hefei 230601, ChinaScience Island Branch of Graduate School, University of Science and Technology of China, Hefei 230031, ChinaScience Island Branch of Graduate School, University of Science and Technology of China, Hefei 230031, ChinaAccurate detection and timely treatment of component defects in substations is an important measure to ensure the safe operation of power systems. In this study, taking substation meters as an example, a dataset of common meter defects, such as a fuzzy or damaged dial on the meter and broken meter housing, is constructed from the images of manual inspection in power systems. There are several challenges involved in accurately detecting defects in substation meter images, such as the complex background, different meter sizes and large differences in the shapes of meter defects. Therefore, this paper proposes the PHAM-YOLO (Parallel Hybrid Attention Mechanism You Only Look Once) network for automatic detection of substation meter defects. In order to make the network pay attention to the key areas against the complex background of the meter defect images and the differences between different defect features, a Parallel Hybrid Attention Mechanism (PHAM) module is designed and added to the backbone of YOLOv5. PHAM integration of local and non-local correlation information can highlight these differences while remaining focused on the meter defect features. To improve the expressive ability of the feature map, a Spatial Pyramid Pooling Fast (SPPF) module is introduced, which pools the input feature map using a continuous fixed convolution kernel, fusing the feature maps of different receptive fields. Bounding box regression (BBR) is the key way to determine object positioning performance in defect detection. EIOU (Efficient Intersection over Union) is, therefore, introduced as a boundary loss function to solve the ambiguity of the CIOU (Complete Intersection Over Union) loss function, making the BBR regression more accurate. The experimental results show that the Average Precision Mean (mAP), Precision (P) and Recall (R) of the proposed PHAM-YOLO network in the dataset are 78.3%, 78.3%, and 79.9%, respectively, with mAP being improved by 2.7% compared to the original model and higher than SSD, Fast R-CNN, etc.https://www.mdpi.com/1424-8220/23/13/6052defect detectionparallel hybrid attention mechanismefficient intersection over unionspatial pyramid pooling fast |
| spellingShingle | Hao Dong Mu Yuan Shu Wang Long Zhang Wenxia Bao Yong Liu Qingyuan Hu PHAM-YOLO: A Parallel Hybrid Attention Mechanism Network for Defect Detection of Meter in Substation Sensors defect detection parallel hybrid attention mechanism efficient intersection over union spatial pyramid pooling fast |
| title | PHAM-YOLO: A Parallel Hybrid Attention Mechanism Network for Defect Detection of Meter in Substation |
| title_full | PHAM-YOLO: A Parallel Hybrid Attention Mechanism Network for Defect Detection of Meter in Substation |
| title_fullStr | PHAM-YOLO: A Parallel Hybrid Attention Mechanism Network for Defect Detection of Meter in Substation |
| title_full_unstemmed | PHAM-YOLO: A Parallel Hybrid Attention Mechanism Network for Defect Detection of Meter in Substation |
| title_short | PHAM-YOLO: A Parallel Hybrid Attention Mechanism Network for Defect Detection of Meter in Substation |
| title_sort | pham yolo a parallel hybrid attention mechanism network for defect detection of meter in substation |
| topic | defect detection parallel hybrid attention mechanism efficient intersection over union spatial pyramid pooling fast |
| url | https://www.mdpi.com/1424-8220/23/13/6052 |
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