To precisely delineate the contours of insulators in complex transmission line images obtained from Unmanned Aerial Vehicle (UAV) inspections and thereby facilitate subsequent defect analysis, this study proposes an instance segmentation framework predicated upon an enhanced SOLOv2 model. The proposed framework integrates a preprocessed edge channel, generated through the Non-Subsampled Contourlet Transform (NSCT), which augments the model’s capability to accurately capture the edges of insulators. Moreover, the input image resolution to the network is heightened to 1200 × 1600, permitting more detailed extraction of edges. Rather than the original ResNet + FPN architecture, the improved HRNet is utilized as the backbone to effectively harness multi-scale feature information, thereby enhancing the model’s overall efficacy. In response to the increased input size, there is a reduction in the network’s channel count, concurrent with an increase in the number of layers, ensuring an adequate receptive field without substantially escalating network parameters. Additionally, a Convolutional Block Attention Module (CBAM) is incorporated to refine mask quality and augment object detection precision. Furthermore, to bolster the model’s robustness and minimize annotation demands, a virtual dataset is crafted utilizing the fourth-generation Unreal Engine (UE4). Empirical results reveal that the proposed framework exhibits superior performance, with AP0.50 (90.21%), AP0.75 (83.34%), and AP[0.50:0.95] (67.26%) on a test set consisting of images supplied by the power grid. This framework surpasses existing methodologies and contributes significantly to the advancement of intelligent transmission line inspection.

The classification of transmission tower bolt images faces challenges such as class imbalance, sample scarcity, and the low pixel proportion of pins. Traditional classification methods exhibit poor performance in identifying key categories with small proportions, fail to leverage the correlation between transmission line fittings and bolts, and suffer from severe false positive issues. This study proposes a novel approach that dynamically integrates two sampling strategies to address the class imbalance problem while incorporating contrastive learning and category labels to enhance the discrimination of easily confused samples. Additionally, an auxiliary branch discrimination mechanism effectively exploits the correlation between fittings and bolts and, combined with a threshold-based decision process, significantly reduces the false positive rate (by 3.74%). The experimental results demonstrate that, compared to the baseline SimCLR framework with ResNet18, the proposed method improves accuracy (Acc) by 10.22%, reduces the false alarm rate by 5%, and significantly enhances classification reliability in transmission line inspections, thereby mitigating unnecessary human resource consumption.