Novel Feature Fusion Module-Based Detector for Small Insulator Defect Detection
| Autor: | Zize Liang, En Li, Guodong Yang, Zishu Gao |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
business.industry
Computer science Feature extraction Detector Normalization (image processing) Pattern recognition Insulator (electricity) Image segmentation Object detection Feature (computer vision) Artificial intelligence Electrical and Electronic Engineering business Instrumentation Block (data storage) |
| Zdroj: | IEEE Sensors Journal. 21:16807-16814 |
| ISSN: | 2379-9153 1530-437X |
| DOI: | 10.1109/jsen.2021.3073422 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|