Transmission line foreign object segmentation based on RB-UNet algorithm.

ABSTRACT:

Background

The identification of foreign objects on transmission lines is crucial for their normal operation. There are risks and difficulties associated with identifying foreign objects on transmission lines due to their scattered distribution and elevated height.

Methods

The dataset for this paper consists of search material from the web, including bird nests, kites, balloons, and rubbish, which are common foreign objects found on top of transmission lines, totaling 400 instances. To enhance the classical U-Net architecture, the coding component has been substituted with a ResNet50 network serving as the feature extraction module. In the decoding section, a batch normalization (BN) layer was added after each convolutional layer in the decoder to improve the model's efficie

SUBMITTER: Wang Y 

PROVIDER: S-EPMC11622974 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature