To address the challenges in the unmanned system-based intelligent inspection of electric transmission line insulators, this paper proposed a multi-geometric reasoning network (MGRN) to accurately detect insulator geometric defects based on aerial images with complex backgrounds and different scales. The spatial geometric reasoning sub-module (SGR) was developed to represent the spatial location relationship of defects. The appearance geometric reasoning sub-module (AGR) and the parallel feature transformation (PFT) sub-module were adopted to obtain the appearance geometric features from the real samples. These multi-geometric features can be fused with the original visual features to identify and locate the insulator defects. The proposed solution is assessed through experiments against the existing solutions and the numerical results indicate that it can significantly improve the detection accuracy of multiple insulator defects using the aerial images.

The detection of insulator umbrella disc shedding is very important to the stable operation of a transmission line. In order to accomplish the accurate detection of the insulator umbrella disc shedding in foggy weather, a two-stage detection model combined with a defogging algorithm is proposed. In the dehazing stage of insulator images, solving the problem of real hazy image data is difficult; the foggy images are dehazed by the method of synthetic foggy images training and real foggy images fine-tuning. In the detection stage of umbrella disc shedding, a small object detection algorithm named FA-SSD is proposed to solve the problem of the umbrella disc shedding occupying only a small proportion of an aerial image. On the one hand, the shallow feature information and deep feature information are fused to improve the feature extraction ability of small targets; on the other hand, the attention mechanism is introduced to strengthen the feature extraction network’s attention to the details of small targets and improve the model’s ability to detect the umbrella disc shedding. The experimental results show that our model can accurately detect the insulator umbrella disc shedding defect in the foggy image; the accuracy of the defect detection is 0.925, and the recall is 0.841. Compared with the original model, it improved by 5.9% and 8.6%, respectively.

With the advancement of high-resolution aerial imaging technology enabled by unmanned aerial vehicles (UAVs), insulator defect detection based on images has emerged as a key approach for intelligent inspection of overhead transmission lines. However, the insulator defective region detection (IDRD) task based on such imagery continues to face several critical challenges, including complex background interference, difficulty in detecting small-scale defects, and class imbalance in sample distribution. To tackle these challenges, we propose a state space model guided deep feature perception network for insulator defect detection (SGFP-YOLO). Specifically, the model introduces a Bi-directional Feature Enhancement Module (BFEM) and a Dual-context Feature Refinement Module (DFRM), both guided by a state-space framework, to enhance global feature and capture local detail features. Additionally, to address the hard-easy sample imbalance problem in the detection process, we introduce Focused Balance Loss, improving the performance of model in both classification and regression tasks. Experiments are conducted on the self-constructed TLID dataset and the publicly available IDID dataset. The results show that SGFP-YOLO outperforms existing advanced models in several metrics, including mAP@0.5 and F1-score, especially in complex background and small-scale defect detection, providing an efficient and accurate solution for intelligent detection of insulators in transmission line.

Intelligent detection of electric power fittings is essential for monitoring conditions and ensuring the stability of transmission and distribution lines. However, the lack of real-world aligned datasets has hindered progress. We developed an electric power fitting dataset that includes challenges, such as long-tailed distribution, multimorphology, multiscale, small targets, and dense occlusion. It contains 1560 unmanned aerial vehicle (UAV)-captured images of 18 types of electric power fittings, annotated with over 13 500 bounding boxes. Compared to existing datasets, ours offers significant advantages in categories, diversity, and complexity. We further propose a multimorphology contrastive learning approach to ensure consistent feature representation and address morphological diversity. By minimizing intraclass variation and maximizing interclass separability, our method achieves more discriminative feature representations. Experimental results indicate that our method achieves an average precision of 86.2% for multifitting detection—improving upon the current state-of-the-art by 2.6%. Meanwhile, cross-dataset testing further verifies the broad applicability of our approach.