Image Fusion and Target Detection Based on Dual ResNet for Power Sensing Equipment
Target detection helps to identify, locate, and monitor key components and potential issues in power sensing networks. The fusion of infrared and visible light images can effectively integrate the target the indication characteristics of infrared images and the rich scene detail information of visib...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/9/2858 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850031982510080000 |
|---|---|
| author | Jie Yang Wei Yan Shuai Yuan Yu Yu Zheng Mao Rui Chen |
| author_facet | Jie Yang Wei Yan Shuai Yuan Yu Yu Zheng Mao Rui Chen |
| author_sort | Jie Yang |
| collection | DOAJ |
| description | Target detection helps to identify, locate, and monitor key components and potential issues in power sensing networks. The fusion of infrared and visible light images can effectively integrate the target the indication characteristics of infrared images and the rich scene detail information of visible light images, thereby enhancing the ability for target detection in power equipment in complex environments. In order to improve the registration accuracy and feature extraction stability of traditional registration algorithms for infrared and visible light images, an image registration method based on an improved SIFT algorithm is proposed. The image is preprocessed to a certain extent, using edge detection algorithms and corner detection algorithms to extract relatively stable feature points, and the feature vectors with excessive gradient values in the normalized visible light image are truncated and normalized again to eliminate the influence of nonlinear lighting. To address the issue of insufficient deep information extraction during image fusion using a single deep learning network, a dual ResNet network is designed to extract deep level feature information from infrared and visible light images, improving the similarity of the fused images. The image fusion technology based on the dual ResNet network was applied to the target detection of sensing insulators in the power sensing network, improving the average accuracy of target detection. The experimental results show that the improved registration algorithm has increased the registration accuracy of each group of images by more than 1%, the structural similarity of image fusion in the dual ResNet network has been improved by about 0.2% compared to in the single ResNet network, and the mean Average Precision (mAP) of the fusion image obtained via the dual ResNet network has been improved by 3% and 6% compared to the infrared and visible light images, respectively. |
| format | Article |
| id | doaj-art-a0fafabff0c14ee6b454e60269b468a2 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-a0fafabff0c14ee6b454e60269b468a22025-08-20T02:58:48ZengMDPI AGSensors1424-82202025-04-01259285810.3390/s25092858Image Fusion and Target Detection Based on Dual ResNet for Power Sensing EquipmentJie Yang0Wei Yan1Shuai Yuan2Yu Yu3Zheng Mao4Rui Chen5School of Communication and Artificial Intelligence, Nanjing Institute of Technology, Nanjing 211167, ChinaSchool of Electric Power Engineering (School of Shen Guorong), Nanjing Institute of Technology, Nanjing 211167, ChinaSchool of Electric Power Engineering (School of Shen Guorong), Nanjing Institute of Technology, Nanjing 211167, ChinaSchool of Communication and Artificial Intelligence, Nanjing Institute of Technology, Nanjing 211167, ChinaSchool of Communication and Artificial Intelligence, Nanjing Institute of Technology, Nanjing 211167, ChinaSchool of Communication and Artificial Intelligence, Nanjing Institute of Technology, Nanjing 211167, ChinaTarget detection helps to identify, locate, and monitor key components and potential issues in power sensing networks. The fusion of infrared and visible light images can effectively integrate the target the indication characteristics of infrared images and the rich scene detail information of visible light images, thereby enhancing the ability for target detection in power equipment in complex environments. In order to improve the registration accuracy and feature extraction stability of traditional registration algorithms for infrared and visible light images, an image registration method based on an improved SIFT algorithm is proposed. The image is preprocessed to a certain extent, using edge detection algorithms and corner detection algorithms to extract relatively stable feature points, and the feature vectors with excessive gradient values in the normalized visible light image are truncated and normalized again to eliminate the influence of nonlinear lighting. To address the issue of insufficient deep information extraction during image fusion using a single deep learning network, a dual ResNet network is designed to extract deep level feature information from infrared and visible light images, improving the similarity of the fused images. The image fusion technology based on the dual ResNet network was applied to the target detection of sensing insulators in the power sensing network, improving the average accuracy of target detection. The experimental results show that the improved registration algorithm has increased the registration accuracy of each group of images by more than 1%, the structural similarity of image fusion in the dual ResNet network has been improved by about 0.2% compared to in the single ResNet network, and the mean Average Precision (mAP) of the fusion image obtained via the dual ResNet network has been improved by 3% and 6% compared to the infrared and visible light images, respectively.https://www.mdpi.com/1424-8220/25/9/2858fusionregistrationstructural similaritytarget detectionsensing insulator |
| spellingShingle | Jie Yang Wei Yan Shuai Yuan Yu Yu Zheng Mao Rui Chen Image Fusion and Target Detection Based on Dual ResNet for Power Sensing Equipment Sensors fusion registration structural similarity target detection sensing insulator |
| title | Image Fusion and Target Detection Based on Dual ResNet for Power Sensing Equipment |
| title_full | Image Fusion and Target Detection Based on Dual ResNet for Power Sensing Equipment |
| title_fullStr | Image Fusion and Target Detection Based on Dual ResNet for Power Sensing Equipment |
| title_full_unstemmed | Image Fusion and Target Detection Based on Dual ResNet for Power Sensing Equipment |
| title_short | Image Fusion and Target Detection Based on Dual ResNet for Power Sensing Equipment |
| title_sort | image fusion and target detection based on dual resnet for power sensing equipment |
| topic | fusion registration structural similarity target detection sensing insulator |
| url | https://www.mdpi.com/1424-8220/25/9/2858 |
| work_keys_str_mv | AT jieyang imagefusionandtargetdetectionbasedondualresnetforpowersensingequipment AT weiyan imagefusionandtargetdetectionbasedondualresnetforpowersensingequipment AT shuaiyuan imagefusionandtargetdetectionbasedondualresnetforpowersensingequipment AT yuyu imagefusionandtargetdetectionbasedondualresnetforpowersensingequipment AT zhengmao imagefusionandtargetdetectionbasedondualresnetforpowersensingequipment AT ruichen imagefusionandtargetdetectionbasedondualresnetforpowersensingequipment |