Insulators play a crucial role in providing electrical isolation in power transmission lines, and timely detection of their defects is vital to avoid severe human life and property losses. In the context of drone inspections of power transmission lines, accurate and timely detection and localization of insulator defects are of paramount importance. Considering the inadequacy of the YOLO series of algorithms in extracting features of insulators and their defects in complex backgrounds, we have designed a method called Insulator Defect-YOLO (ID-YOLO) to address this challenge. First, we develop the Global Convolution (GConv) module to integrate spatial and channel information, thereby enhancing the effectiveness of feature extraction. Second, we built the C3-Global Pooling Fusion (C3-GPF) module, aimed at strengthening focus on key data during the feature extraction and fusion stages. Third, we develop the Multi-Scale Information Fusion (MSIF) module to balance the algorithm’s detection accuracy and speed, ensuring superior performance in practical applications. Fourth, we built the Weighted Feature Information Fusion (WFIF) module to further enhance the fusion capability of key information. Finally, we adopt the SCYLLA-IoU (SIoU) loss function to replace the original CIoU, thereby improving the algorithm’s localization precision and accelerating convergence speed. Experimental results indicate that ID-YOLO achieves an average precision of 90.9%, representing a 3.3% improvement over the baseline YOLOv5s algorithm. Additionally, ID-YOLO achieves a detection speed of 90 frames per second, meeting the requirements for real-time detection. Practical test results demonstrate that the ID-YOLO algorithm significantly improves detection precision while effectively addressing the challenges associated with multi-object and small-object detection, showcasing its potential application in detecting insulator defects in power transmission lines.

The authors introduce their design, implementation and formally verification of a Trusted Execution Environment (TEE)‐based trusted storage system (TSS) in mobile devices, which conforms to GlobalPlatform specifications. The authors’ TSS provides not only authenticating the integrity and freshness of data but also many security storage operation properties like atomicity operations of a persistent object. To improve data store efficient when a big persistent object is read or written, a new mechanism that dynamic allocate continuous memory in REE's kernel memory space and map the address to the TEE through a communication pipe is proposed. This method can reduce switching times, allocating memory times and copy memory overloads between two worlds. A formal method is used in their design and development to guarantee the correctness and security of TSS. They consider the functional correctness mainly in this study, and use traditional formal verification tool – VCC verify the functional correctness of TSS. Their evaluation demonstrates its advantage compared to existing systems in addition.

Target defect detection of overhead lines is crucial for the safe and reliable operation of power grids. In order to improve the detection efficiency of insulator target defects, a MACE-YOLO network insulator detection model for overhead lines is proposed. Firstly, MobileNetV3 is proposed to lighten the YOLOv5s backbone network to reduce the model parameters; secondly, an ASFF adaptive feature fusion structure is designed to equalize the inconsistency of different feature layers; then a CBAM attention mechanism is added to the head network to increase the weight of the target information so as to improve the model accuracy; lastly, the edge loss function is adopted as EIOU- Loss loss function to accelerate the convergence of the network. The experimental results show that the GFLOPs of the MACE-YOLO network is 11.6, which is 1.3% higher than the YOLOv5s algorithm, and the Map_0.5 is 96.7%, which is 0.2 percentage points higher than the YOLOv5s algorithm, so the proposed MACE-YOLO network has a higher detection speed while guaranteeing the accuracy of the model detection, which can meet the requirements in the requirements of detection accuracy in lightweight target detection scenarios.