Since the inception, the most widely used techniques for inspection are the manual inspection which includes either foot patrol or/and conventional helicopter-based inspection.

The detailed approaches used for segmentation, object detection, and anomaly detection are described in the following section.Powerlines SegmentationThe Powerlines Segmentation is used to separate the powerlines (the power transmission cables) from the entire image so that an inspection algorithm can just focus and analyze only the power lines while staying unaffected by the surrounding or the background of the powerlines.

It consists of convolutional layers on skip pathways which bridges the semantic gap between encoder and decoder feature maps, thus aiding in improving the gradient flow.DatasetsDataset 1:We have obtained 200 Visible Light (VL) spectrum images from the https://data.mendeley.com/datasets/twxp8xccsw/1.

It consists of 200 images of size 512×512, along with the binary wired image masks for all the input images.Dataset 2:The other set of images was obtained from  https://data.mendeley.com/datasets/n6wrv4ry6v/8.

Further, data augmentations were carried out on Dataset 2 same as Dataset 1.Training and Segmentation Methodology Nested U-Net architecture, as the name implies, makes use of nested and dense skip connections between encoder and decoder apart from the typical skip connection used in U-Net Network.

Like the IoU, they both range from 0 to 1, with 1 signifying the greatest similarity between predicted and truth.