Anti-vibration hammer defects pose a significant risk to the safe operation of power transmission lines. Addressing the issues posed by the various manifestations of identical category defects and the similarities among different defects, this paper introduces a defect detection algorithm for anti-vibration hammers based on structural knowledge representation. Initially, a Structural Knowledge Enhancement (StKE) Module is proposed to conduct a statistical analysis of the aspect ratios of different defects in anti-vibration hammers, effectively extracting the structural features of the anti-vibration hammers and their corresponding defects. Subsequently, a Structural Knowledge Representation (StKR) Module is introduced, which bolsters the model’s ability to precisely locate defects that disrupt structural symmetry. The model incorporates the Coordinate Attention (CA) mechanism to acquire contextual information, thereby enhancing detection accuracy. Experimental results demonstrate that the improved model achieves a 6.1% increase in mean detection precision over the baseline model, and a notable improvement in detection accuracy compared to other advanced algorithms.

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 [email protected] 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.

Edge detection is frequently employed to support downstream visual tasks. However, current edge detection methods still encounter two significant challenges: extracting complex textured targets and capturing valuable information from complex backgrounds. We propose FFED, a flow field-guided edge detection model. FFED integrates the three components of our design. FFED incorporates three designed components: the Feature Broadcast Module (FBM), the Antagonistic Bio-inspired Spatial Attention Module (ABSAM), a novel pixel difference convolution named ALS. The FBM serves as an implementation mode of the flow field, with its input pair selection strategy inspired by video processing. The FBM broadcasts high-level semantic features to high-resolution ones, preserving more meaningful texture details. Inspired by biological studies, we propose the ABSAM. ABSAM extracts valuable information from complex backgrounds by optimizing spatial modeling of data. The ALS exhibits enhanced capability in extracting gradient information and capturing subtle texture details that are easily overlooked. Experimental results demonstrate that FFED achieved competitive detection results on NYUD, BSDS500, and BIPED datasets, as well as good performance on industrial datasets. Additionally, the experiment verified the auxiliary effect of FFED on downstream visual tasks. The code is available at https://github.com/hanyuchen2022/Flow-field-guided-edge-detection-FFED-.

Short-term load forecasting has become increasingly important since the rise of the competitive energy markets and has become one of the major areas of research in recent years. Toward to this important topic, this paper proposes a new approach- the self-organizing fuzzy neural network (SOFNN) modeling method for the short-term load forecasting. The main advantages of this approach are that, firstly, it is very user friendly as SOFNN can automatically determine the model structure and identify the model parameters without requiring the in-depth knowledge about fuzzy systems and neural networks; secondly, it provides the excellent forecasting accuracy. Applying this approach based on the real data, the achieved average mean absolute percentage error (MAPE) for load forecasting is less than 1%.