تقرير
Novel Feature Fusion Module-Based Detector for Small Insulator Defect Detection
| العنوان: | Novel Feature Fusion Module-Based Detector for Small Insulator Defect Detection |
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| المؤلفون: | Gao, Zishu, Yang, Guodong, Li, En, Liang, Zize |
| بيانات النشر: | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| سنة النشر: | 2021 |
| المجموعة: | Institute of Automation: CASIA OpenIR (Chinese Academy of Sciences) / 中国科学院自动化研究所机构知识库 |
| مصطلحات موضوعية: | Feature extraction, Insulators, Sensors, Image segmentation, Inspection, Fuses, Support vector machines, Insulator defect detection, anchor-free object detection, data augmentation, aerial image, Engineering, Instruments & Instrumentation, Physics, Electrical & Electronic, Applied |
| الوصف: | The failure of an insulator may compromise the safety of the entire power transmission system. Therefore, insulator defect detection is vital for the safe operation of power systems. However, insulator defects in an insulator image may have varying sizes, and several currently available methods do not have satisfactory detection accuracy for small defects. To address this issue, we propose an improved detection network for small insulator defects with a batch normalization convolutional block attention module (BN-CBAM) and a feature fusion module. The BN-CBAM is designed to better exploit channel information and enhance the effect of different channels on the feature map. In addition, we propose a feature fusion module that fuses multi-scale features from different layers to improve small object detection performance. Moreover, to address the scarcity of aerial images, a data augmentation method based on the fusion of the target segment and background is introduced. Experiments demonstrate that the proposed method achieves better small insulator defect detection performance than other state-of-the-art approaches. In addition, data augmentation methods enrich sample diversity and enhance the generalizability of the network. |
| نوع الوثيقة: | report |
| اللغة: | English |
| العلاقة: | IEEE SENSORS JOURNAL; http://ir.ia.ac.cn/handle/173211/45554Test; http://ir.ia.ac.cn/handle/173211/45555Test |
| DOI: | 10.1109/JSEN.2021.3073422 |
| الإتاحة: | https://doi.org/10.1109/JSEN.2021.3073422Test http://ir.ia.ac.cn/handle/173211/45554Test http://ir.ia.ac.cn/handle/173211/45555Test |
| رقم الانضمام: | edsbas.6DD9F044 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1109/JSEN.2021.3073422 |
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