In view of the slow convergence speed of traditional particle swarm optimization algorithms, which makes it easy to fall into local optimum, this paper proposes an OTSU multi-threshold image segmentation based on an improved particle swarm optimization algorithm. After the particle swarm completes the iterative update speed and position, the method of calculating particle contribution degree is used to obtain the approximate position and direction, which reduces the scope of particle search. At the same time, the asynchronous monotone increasing social learning factor and the asynchronous monotone decreasing individual learning factor are used to balance global and local search. Finally, chaos optimization is introduced to increase the diversity of the population to achieve OTSU multi-threshold image segmentation based on improved particle swarm optimization (IPSO). Twelve benchmark functions are selected to test the performance of the algorithm and are compared with the traditional meta-heuristic algorithm. The results show the robustness and superiority of the algorithm. The standard dataset images are used for multi-threshold image segmentation experiments, and some traditional meta-heuristic algorithms are selected to compare the calculation efficiency, peak signal to noise ratio (PSNR), structural similarity (SSIM), feature similarity (FSIM), and fitness value (FITNESS). The results show that the running time of this paper is 30% faster than other algorithms in general, and the accuracy is also better than other algorithms. Experiments show that the proposed algorithm can achieve higher segmentation accuracy and efficiency.

Power transformers are complex and extremely important piece of electrical equipment in a power system, playing an important role in changing voltage and transmitting electricity. Its operational status directly affects the stability and safety of power grids, and once a fault occurs, it may lead to significant economic losses and social impacts. The traditional detection methods rely on the technical level of power system operation and maintenance personnel, and are based on Dissolved Gas Analysis (DGA) technology, which analyzes the components of dissolved gases in transformer oil for preliminary fault diagnosis. However, with the increasing accuracy and intelligence requirements for transformer fault diagnosis in power grids, the DGA analysis method is no longer able to meet the requirements. Therefore, this article proposes an improved transformer fault diagnosis method based on a residual BP neural network. This method deepens the BP neural network by stacking multiple residual network modules, and fuses and expands gas feature information through an improved BP neural network. In the improved residual BP neural network, SVM is introduced to judge the extracted feature vectors at each layer, screen out feature vectors with high accuracy, and increase their weights. The feature vector with the highest cumulative weight is selected as an input for transformer fault diagnosis. This method utilizes multi-layer neural network mapping to extract gas feature information with more significant feature differences after fusion expansion, thereby effectively improving diagnostic accuracy. The experimental results show that, compared with traditional BP neural network methods, the proposed algorithm has higher accuracy in transformer fault diagnosis, with an accuracy rate of 92%, which can ensure the sustainable, normal, and safe operation of power grids.

Target detection is one of the research hotspots in remote sensing image information extraction, which has a wide range of application prospects. Compared with natural images, remote sensing images are characterized by many dense small targets, large changes in target scale, and fuzzy target appearance under complex backgrounds. Therefore, this paper proposes the YOLO-FSD remote sensing image target detection algorithm. Firstly, to address the problem of fuzzy target appearance under complex background, a Swin-CSP structure is introduced in each layer of the network, which enhances the model’s discriminative ability by learning the target’s local and global attributes. Then, the Faster-SPPCSPC module is designed to efficiently solve the problem of large target scale variation by improving the feature pyramid. Finally, a new DWC-Head is utilized to reduce the prediction bias due to tiny and dense targets and to improve the model’s localization and classification ability in complex backgrounds. Our extensive experiments show that our proposed YOLO-FSD algorithm improves AP and mAP by 3.9% and 3.3%, respectively, on the VisDrone dataset compared to the original YOLOv7 algorithm. The AP and mAP are improved by 3.6% and 4.2% on the DOTA dataset, and the inference speed reaches 15.3ms, which has better detection accuracy and inference speed.

Vehicle detection is important for the development of Intelligent Transportation Systems (ITS), which has made great strides in recent years. However, at night, vehicle detection faces many difficulties such as low illumination, street lights, and the appearances vehicle headlights, etc. In order to solve these problems, we propose an improved nighttime vehicle detection algorithm based on Faster R-CNN. Firstly, we combine the Deformable Convolutional Network with Faster R-CNN to improve the detection accuracy features of night vehicles of different sizes and shapes. Secondly, to improve the prediction accuracy of bounding box position information, we adopt Side-Aware Boundary Localization to replace the traditional bounding box prediction. It can further obtain more accurate position information. At the same time, aiming at the imbalance of samples in the training process, we use Oline Hard Example Mining(OHEM) to train samples with a high probability of error to improve the learning effect of a few classes; and to improve the accuracy of night vehicle detection. In addition, we use Soft Non-Maximum Suppression(Soft-NMS) to reduce the number of missed vehicles. The improved algorithm efficiently improves the night vehicle detection accuracy and reduces the model complexity. Furthermore, we verify the effectiveness of each innovation module through ablation experiments and comparison experiments. Finally, the advantages of the improved model in terms of nighttime vehicle detection accuracy are verified by experimenting on the open-source intelligent traffic dataset UA-DETRAC and the open-source diverse automated driving dataset BDD100K.

Object detection for remote sensing images has problems such as complex backgrounds, multi-scale and difficulty in the detection of small targets. Because of the above problems, an improved object detection algorithm for remote-sensing images is proposed. Firstly, fusing the characteristics of shallow and deep networks, the original feature pyramid structure is reconstructed, the improved adaptive feature fusion structure is introduced before the network prediction, and the location and category information of different feature maps are fused. Secondly, the attention mechanism is introduced into the network to reduce the interference caused by complex backgrounds; Finally, the up-sampling module is improved to expand the receptive field and realize up-sampling based on semantic information. Through experiments, we show that our network achieves 93.9% and 98.5% detection accuracy on Levir and RSOD dataset, respectively, with the detection speed, reaching 98.26fps. Compared with the original network, the accuracy is increasing by 4% and 1.2%, respectively. Experimental results show that the detection accuracy of our algorithm is improved while maintaining the light weights and efficiencies of the original network.