To address the challenges in detecting steel strand damage in high-voltage overhead lines

an enhanced YOLOv7model for detecting steel strand damage is proposed.Given the issue of small-scale damage during the detection process

aCoordinate Attention module is employed to enhance the network's ability to extract information from small-scale targets.Basedon the pyramid feature fusion architecture of the path aggregation feature pyramid network(PaFPN)

an adaptively spatialfeature fusion(ASFF)module is added to enhance the ability of the module to handle images of different sizes and variousdamage scales. Additionally

a weighted intersection over union is used to replace the complete intersection over union inoptimizing the loss function

and a dynamic focusing mechanism is applied to bounding box regression to enhance themodule's robustness.Experimental results demonstrate a significant improvement in the performance of the modified YOLOv7module compared to the original YOLOv7 module in detecting steel strand damage

with a 15.8% increase in mean averageprecision.The proposed model outperforms the original model in the effectiveness of detecting steel strand damage.