The task of the detection of unmanned aerial vehicles (UAVs) is of great significance to social communication security. Infrared detection technology has the advantage of not being interfered with by environmental and other factors and can detect UAVs in complex environments. Since infrared detection equipment is expensive and data collection is difficult, there are few existing UAV-based infrared images, making it difficult to train deep neural networks; in addition, there are background clutter and noise in infrared images, such as heavy clouds, buildings, etc. The signal-to-clutter ratio is low, and the signal-to-noise ratio is low. Therefore, it is difficult to achieve the UAV detection task using traditional methods. The above challenges make infrared UAV detection a difficult task. In order to solve the above problems, this work drew upon the visual processing mechanism of the human brain to propose an effective framework for UAV detection in infrared images. The framework first determines the relevant parameters of the continuous-coupled neural network (CCNN) through the image’s standard deviation, mean, etc. Then, it inputs the image into the CCNN, groups the pixels through iteration, then obtains the segmentation result through expansion and erosion, and finally, obtains the final result through the minimum circumscribed rectangle. The experimental results showed that, compared with the existing most-advanced brain-inspired image-understanding methods, this framework has the best intersection over union (IoU) (the intersection over union is the overlapping area between the predicted segmentation and the label divided by the joint area between the predicted segmentation and the label) in UAV infrared images, with an average of 74.79% (up to 97.01%), and can effectively realize the task of UAV detection.

As affected by limited information and the complex background, the accuracy of small-target water-floating garbage detection is low. To increase the detection accuracy, in this research, a small-target detection method based on APM-YOLOv7 (the improved YOLOv7 with ACanny PConv-ELAN and MGA attention) is proposed. Firstly, the adaptive algorithm ACanny (adaptive Canny) for river channel outline extraction is proposed to extract the river channel information from the complex background, mitigating interference of the complex background and more accurately extracting the features of small-target water-floating garbage. Secondly, the lightweight partial convolution (PConv) is introduced, and the partial convolution-efficient layer aggregation network module (PConv-ELAN) is designed in the YOLOv7 network to improve the feature extraction capability of the model from morphologically variable water-floating garbage. Finally, after analyzing the limitations of the YOLOv7 network in small-target detection, a multi-scale gated attention for adaptive weight allocation (MGA) is put forward, which highlights features of small-target garbage and decreases missed detection probability. The experimental results showed that compared with the benchmark YOLOv7, the detection accuracy in the form of the mean Average Precision (mAP) of APM-YOLOv7 was improved by 7.02%, that of mmAP (mAP0.5:0.95) was improved by 3.91%, and Recall was improved by 11.82%, all of which meet the requirements of high-precision and real-time water-floating garbage detection and provide reliable reference for the intelligent management of water-floating garbage.

Dunhuang mural inpainting aims to fill in the missing regions of damaged murals with realistic content. Denoising probabilistic diffusion model (DDPM) has made great strides in semantic generation and shown promising results in image inpainting. However, three potential challenges prevent existing diffusion-based methods from restoring the Dunhuang murals: 1) effective visual information cannot be accurately extracted due to historical reasons, with most of the pixels being faded; 2) there are semantic discrepancy between damaged and visible regions in the inpainting results; and 3) the original structure and style of the damaged regions cannot be adequately restored. To this end, we propose a novel adversarial diffusion model for mural inpainting, which consists of: 1) a mural enhancement module named pixel-enhanced fire-controlled pulse-coupled neural network (PEFCPCNN), designed to enhance faded pixels to accurately extract the visual features of the mural; 2) a novel adversarial diffusion framework that optimizes the sampling prediction of mural over time steps; and 3) line drawing and different loss functions to constrain the reconstructed content to approximate the structure and style of original mural. The variational transform layer (VTL) and multi-scale contextual feature aggregation (MCFA) module are proposed to reconstruct content that is structurally coherent and texturally reasonable. Experiments on the Dunhuang mural dataset demonstrate that the proposed method outperforms state-of-the-art methods in terms of both the semantic reasonableness and global semantic consistency of inpainting content.

In industrial scenarios, bearing fault diagnosis often suffers from data scarcity and class imbalance, which significantly hinders the generalization performance of data-driven models. While generative adversarial networks (GANs) have shown promise in data augmentation, their efficacy deteriorates in the presence of multi-category and structurally complex fault distributions. To address these challenges, this paper proposes a novel fault diagnosis framework based on a Multi-Domain Feature Transformer GAN (MDFT-GAN). Specifically, raw vibration signals are transformed into 2D RGB representations via joint time-domain, frequency-domain, and time–frequency-domain mappings, effectively encoding multi-perspective fault signatures. A Transformer-based feature extractor, integrated with Efficient Channel Attention (ECA), is embedded into both the generator and discriminator to capture global dependencies and channel-wise interactions, thereby enhancing the representation quality of synthetic samples. Furthermore, a gradient penalty (GP) term is introduced to stabilize adversarial training and suppress mode collapse. To improve classification performance, an Enhanced Hybrid Visual Transformer (EH-ViT) is constructed by coupling a lightweight convolutional stem with a ViT encoder, enabling robust and discriminative fault identification. Beyond performance metrics, this work also incorporates a Grad-CAM-based interpretability scheme to visualize hierarchical feature activation patterns within the discriminator, providing transparent insight into the model’s decision-making rationale across different fault types. Extensive experiments on the CWRU and Jiangnan University (JNU) bearing datasets validate that the proposed method achieves superior diagnostic accuracy, robustness under limited and imbalanced conditions, and enhanced interpretability compared to existing state-of-the-art approaches.