Face image inpainting technology is an important research direction in image restoration. When the current image restoration methods repair the damaged areas of face images with weak texture, there are problems such as low accuracy of face image decomposition, unreasonable restoration structure, and degradation of image quality after inpainting. Therefore, this paper proposes an adaptive face image inpainting algorithm based on feature symmetry. Firstly, we locate the feature points of the face, and segment the face into four feature parts based on the feature point distribution to define the feature search range. Then, we construct a new mathematical model, introduce feature symmetry to improve priority calculation, and increase the reliability of priority calculation. After that, in the process of searching for matching blocks, we accurately locate similar feature blocks according to the relative position and symmetry criteria of the target block and various feature parts of the face. Finally, we introduced the HSV (Hue, Saturation, Value) color space to determine the best matching block according to the chroma and brightness of the sample, reduce the repair error, and complete the face image inpainting. During the experiment, we firstly performed visual evaluation and texture analysis on the inpainting face image, and the results show that the face image inpainting by our algorithm maintained the consistency of the face structure, and the visual observation was closer to the real face features. Then, we used the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) as objective evaluation indicators; among the five sample face images inpainting results given in this paper, our method was better than the reference methods, and the average PSNR value improved from 2.881–5.776 dB using our method when inpainting 100 face images. Additionally, we used the time required for inpainting the unit pixel to evaluate the inpainting efficiency, and it was improved by 12%–49% with our method when inpainting 100 face images. Finally, by comparing the face image inpainting experiments with the generative adversary network (GAN) algorithm, we discuss some of the problems with the method in this paper based on graphics in repairing face images with large areas of missing features.

With the increasing need for real-time crowd evaluation in military surveillance, public safety, and event crowd management, crowd counting using unmanned aerial vehicle (UAV) captured images has emerged as an essential research topic. While conventional RGB-based methods have achieved significant success, their performance is severely hampered in low-light environments due to poor visibility. Integrating thermal infrared (TIR) images can address this issue, but existing RGB-T crowd counting networks, which employ multi-stream architectures, tend to introduce computational redundancy and excessive parameters, rendering them impractical for UAV applications constrained by limited onboard resources. To overcome these challenges, this research introduces an innovative, compact RGB-T framework designed to minimize redundant feature processing and improve multi-modal representation. The proposed approach introduces a Partial Information Interaction Convolution (PIIConv) module to selectively minimize redundant feature computations and a Global Collaborative Fusion (GCFusion) module to improve multi-modal feature representation through spatial attention mechanisms. Empirical findings indicate that the introduced network attains competitive results on the DroneRGBT dataset while significantly reducing floating-point operations (FLOPs) and improving inference speed across various computing platforms. This study’s significance is in providing a computationally efficient framework for RGB-T crowd counting that balances accuracy and resource efficiency, making it ideal for real-time UAV deployment.

In IoT-driven intelligent perception systems, multi-modal crowd counting using visible-thermal (RGB-T) sensor arrays deployed on edge nodes plays a vital role in urban management. Existing methods primarily focus on inter-modal feature fusion but suffer from noise amplification when directly integrating thermal features with degraded visible images under low-light conditions, leading to inaccurate density estimation and poor nighttime performance. To address this, we propose the first domain-adaptive network for RGB-T crowd counting. First, we introduce a dark-light domain adaptation module based on Retinex decomposition. Unlike traditional approaches that extract only bright and thermal domain features, our module employs a dual decomposition-recomposition process to learn illumination-invariant features from RGB images, enhancing discriminability in dark regions while preserving bright-domain semantics. Second, the module is supervised by feature alignment, reflectance consistency, and decomposition invariance losses, strengthening its robustness under extreme illumination degradation. Finally, a multi-domain progressive decision fusion module is designed to leverage shared and domain-specific features through public feature extraction, dual-domain fusion, and weighted decision processes, improving generalization across varying lighting conditions. Trained solely on well-lit data, our method generalizes effectively to dark scenarios. Experiments on DroneRGBT and RGBTCC datasets demonstrate superior performance over state-of-the-art fusion methods, particularly excelling in low-light crowd counting.

Crowd counting is a significant task in computer vision. By combining the rich texture information from RGB images with the insensitivity to illumination changes offered by thermal imaging, the applicability of models in real-world complex scenarios can be enhanced. Current research on RGB-T crowd counting primarily focuses on feature fusion strategies, multi-scale structures, and the exploration of novel network architectures such as Vision Transformer and Mamba. However, existing approaches face two key challenges: limited robustness to illumination shifts and insufficient handling of scale discrepancies. To address these challenges, this study aims to develop a robust RGB-T crowd counting framework that remains stable under illumination shifts, through introduces two key innovations beyond existing fusion and multi-scale approaches: (1) a cross-modal adaptive fusion module (CMAFM) that actively evaluates and fuses reliable cross-modal features under varying scenarios by simulating a dynamic feature selection and trust allocation mechanism; and (2) a multi-scale aggregation module (MSAM) that unifies features with different receptive fields to an intermediate scale and performs weighted fusion to enhance modeling capability for cross-modal scale variations. The proposed method achieves relative improvements of 1.57% in GAME(0) and 0.78% in RMSE on the DroneRGBT dataset compared to existing methods, and improvements of 2.48% and 1.59% on the RGBT-CC dataset, respectively. It also demonstrates higher stability and robustness under varying lighting conditions. This research provides an effective solution for building stable and reliable all-weather crowd counting systems, with significant application prospects in smart city security and management.

Current RGB-T multimodal crowd counting methods utilize multi-stream network architectures and complex fusion techniques to improve counting accuracy, which leads to significantly increased computational burden and reduced model efficiency, making it difficult to meet real-time requirements in smart city edge computing scenarios. To address this, we propose SONet, a single-stream network with one-step multimodal fusion. The fusion process leverages state space models for efficient cross-modal modeling. A cross scan-and-aggregate module (CSAM) establishes long-range dependencies across modalities to enable global semantic interaction, while a graph scan-and-fusion module (GSFM) adaptively captures fine-grained local structural relationships for dynamic feature fusion. Together, these modules achieve effective global–local feature integration with minimal overhead, and introduce only 5.52M additional parameters based on a VGG-16 backbone, significantly improving multimodal crowd counting performance on the DroneRGBT and RGBT-CC benchmark datasets. On the resource-constrained Jetson Nano edge platform, SONet significantly reduces memory footprint and accelerates inference speed. Further validation on lightweight backbones confirms the adaptability and application potential of the proposed method, offering a parameter-efficient, high-speed, and accurate solution for RGB-T crowd counting on edge devices in smart city applications. Source code available at https://github.com/max-niu/SONet.