In low-light conditions, the detection scene can be harsh, some fundamental image features of the target to be lost, which can result in the disappearance of essential visual characteristics of the object to be detected. They have failed to balance the connection between the low-level semantic information of low-light images and normal images. This article proposes an algorithm for weak-supervised and adaptive object detection in the low-light environment for YOLOV7 (WSA-YOLO) that utilizes adaptive enhancement to effectively improve object detection capability in low-light environments, addressing this practical issue. The proposed decomposition network decomposes the image into reflectance and illumination maps, which are then enhanced separately. The proposed adaptive residual feature block (ARFB) effectively utilizes the feature correlation between low-light and normal-light images and shares the weights between them to improve parameter reuse capability during parameter prediction using the parameter prediction block. The proposed adaptive adjustment block and consistency loss function are used together to enhance the brightness and suppress noise. Finally, the you only look once (YOLO) framework is utilized for object classification, regression, and prediction. Using the metric mean average precision (mAP) for evaluation on the recognized datasets, the proposed WSA-YOLO has a performance improvement of about 8% in peak signal-to-noise ratio (PSNR), structural similarity index, and natural image quality evaluator (NIQE). And the increase in mAP is about 9%.

Traditional power equipment defect-detection relies on manual verification, which places a high demand on the verifier’s experience, as well as a high workload and low efficiency, which can lead to false detection and missed detection. The Mask of the regions with CNN features (Mask RCNN) deep learning model is used to provide a defect-detection approach based on the Mask RCNN of Attention, Rotation, Genetic algorithm (ARG-Mask RCNN), which employs infrared imaging as the data source to assess the features of damaged insulators. For the backbone network of Mask RCNN, the structure of Residual Network 101 (ResNet101) is improved and the attention mechanism is added, which makes the model more alert to small targets and can quickly identify the location of small targets, improve the loss function, integrate the rotation mechanism into the loss function formula, and generate an anchor frame where a rotation angle is used to accurately locate the fault location. The initial hyperparameters of the network are improved, and the Genetic Algorithm Combined with Gradient Descent (GA-GD) algorithm is used to optimize the model hyperparameters, so that the model training results are as close to the global best as possible. The experimental results show that the average accuracy of the insulator fault-detection method proposed in this paper is as high as 98%, and the number of frames per second (FPS) is 5.75, which provides a guarantee of the safe, stable, and reliable operation of our country’s power system.

With modern cryptography evolves, some sensitive information needs to be protected with secure and efficient algorithms. In this context, we found that Hopfield neural network (HNN) has stronger memory and can generate luxuriant kinetic behavior, especially with the introduction of fractional-order operators. Therefore, we propose a chaotic image encryption based on the fractional-order HNN (FO-HNN), where FO-HNN appears as a key generator. To de-correlate the correlation between pixels, a spatial permutation strategy is designed first, and then a new diffusion technique based on a Three-input logic valve is adopted to guide the diffusion process. Simulation results and security analysis show that the HNN-based image cryptosystem has superior security performance.

Insulators play an important role in supporting and securing live conductors. In the task of insulator fault detection, existing models often miss small targets due to the interference of complex backgrounds. Furthermore, due to the very large aspect ratio of the insulators themselves, most models contain extraneous background in their results. To solve the above problems, this paper makes full use of the characteristics of the insulator itself and optimizes YOLOv5 from four aspects: feature extraction, feature fusion, candidate frame generation, and loss function. We divide the image feature extraction process into two procedures. The first procedure is mainly responsible for extracting insulator images from complex backgrounds, and the second stage is based on the extracted insulator images for fault analysis. At the same time, considering the trouble that the objective is too small due to the shooting distance, we design an attention mechanism that is more suitable for practical needs to enhance the ability to capture small objects. Furthermore, we design a rotated candidate box to improve the quality of the predicted box and reduce irrelevant background. The FAIN_detection dataset is compared with various advanced detection methods. The proposed method is more effective than the previous model by verifying the self-made data. Notably, the model achieved an average accuracy of 98% at 29 frames per second (FPS), laying the foundation for automatic insulator fault detection.