AI Article Synopsis
- The reliability of insulators is crucial for the stable operation of electric power systems, and detecting defective insulators is a key challenge in smart grid technology.
- A new detection method using CenterNet improves accuracy and efficiency by simplifying the backbone network and incorporating an attention mechanism to filter out unnecessary information.
- The method achieved a detection accuracy of 96.16% and a processing speed of 30FPS on a NVIDIA GTX 1080, significantly outperforming traditional detection models like Faster R-CNN and YOLOV3.
Article Abstract
The reliability of the insulator has directly affected the stable operation of electric power system. The detection of defective insulators has always been an important issue in smart grid systems. However, the traditional transmission line detection method has low accuracy and poor real-time performance. We present an insulator defect detection method based on CenterNet. In order to improve detection efficiency, we simplified the backbone network. In addition, an attention mechanism is utilized to suppress useless information and improve the accuracy of network detection. In image preprocessing, the blurring of some detected images results in the samples being discarded, so we use super-resolution reconstruction algorithm to reconstruct the blurred images to enhance the dataset. The results show that the AP of the proposed method reaches 96.16% and the reasoning speed reaches 30FPS under the test condition of NVIDIA GTX 1080 test conditions. Compared with Faster R-CNN, YOLOV3, RetinaNet and FSAF, the detection accuracy of proposed method is greatly improved, which fully proves the effectiveness of the proposed method.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8320933 | PMC |
| http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0255135 | PLOS |
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