Inspection of power line insulator using an enhanced faster region-convolutional neural network

Inspection of power line insulators is crucial to the safe and reliable operation of power transmission networks. Manual inspection techniques are deficient in terms of efficiency, accuracy, availability, security, and cost. To overcome these problems, this article proposes an improved version, Enhanced Faster R-CNN I2D-Net, which uses faster region-based convolutional neural network (Faster R-CNN) with some quality of feature integration and attention-based features to achieve consistent insulator defects detection in complicated scenes. The suggested architecture is an integration of a residual network backbone (ResNet-50) which extracts features, a bidirectional feature pyramid network (BiFPN) which learns fusion weights to achieve adaptive multiscale fusion of features and receptive field attention plus (RFA+) which optimises the features and improves the overall detection performance. The model also has an Improved Context Perception Module (I-CPM) that comes after the Region Proposal Network (RPN). Also employs dilated convolution to achieve better categorization and bounding-box regression. Additionally, Gradient-weighted Class Activation Mapping (Grad-CAM) is utilized on the feature maps produced by the RFA+ module to illustrate defect-associated areas and create heatmaps that pinpoint defect evidence. This work is new because it successfully combines multi-scale feature fusion, attention mechanisms, and contextual modeling into one framework for strong insulator defect detection. The suggested method works better than the ones that are already out there. It has a mean Average Precision (mAP) of 98.03%, a precision of 99.05%, and a recall of 98.96% for finding faults.