Wenyue Niu

In multispectral image fusion, edge distortion, spectral loss, and geometric mismatch seriously affect the fusion accuracy, especially in complex road scenes with shadows or occlusions. Multispectral image fusion aims to preserve surface details and spectral data. In order to solve the problems of edge distortion and spectral loss in image fusion, this paper proposes a multispectral and hyperspectral fusion method based on edge chromatic aberration. Swin Transformer is used for multi-scale feature extraction, and the GRDB module is added to preserve edge details, which improves the clarity and accuracy of diseased edges in road scene fusion images. In addition, saliency weight mapping can identify and highlight key disease areas, ensuring that they are prominent in the fused image.Experiments show that the multispectral image fusion method based on edge color difference significantly improves the performance of road damage analysis on the self-built BUCEA-MS-Road-Damage dataset: the edge IoU in the detection task is increased to 80.1% (+1.3%), and the target detection accuracy is 92.3% (+3.6%); the accuracy and recall of the classification task are increased to 91.3% (+3.0%) and 89.8% (+3.0%) respectively; the Dice coefficient of the segmentation task is 83.3% (+3.0%). In the cross-sensor test, the fusion result is still robust (SSIM=0.93, SAM=2.7°), and the edge color difference index (ECD-Index=6.3) is 25.9% lower than the baseline. This method effectively solves the problems of multi-scale feature extraction and texture distortion of cracks through adaptive color difference correction and spectral consistency constraints, providing high-precision data support for intelligent road maintenance.

To address the issues of uneven feature point distribution, environmental interference, and insufficient real-time performance in UAV image stitching in complex urban environments, this paper proposes an improved ORB algorithm based on Gaussian scale-space optimization and dynamic grid division, combined with a global geometric consistency optimization strategy. First, local adaptive noise filtering and bilateral filtering are applied to enhance image quality. Then, multi-scale feature detection is achieved using a Gaussian scale-space pyramid, and dynamic grid division is employed to balance feature point distribution. Finally, a global energy function, including reprojection error and smoothness constraints, is constructed to iteratively optimize the homography matrix and suppress stitching distortions. Experimental results show that the proposed method achieves high processing speed on low-performance hardware platforms, improves feature point distribution uniformity to 0.89, and achieves stitching accuracy (RMSE) of 3.5 pixels, significantly outperforming ORB and SIFT algorithms, while remaining robust in dynamic occlusion and lighting variation scenarios. This method provides a lightweight and efficient solution for UAV image stitching in urban environments, supporting applications such as urban planning and disaster assessment. Future work will explore lightweight deep learning integration and edge computing acceleration to further improve dynamic scene adaptability.

The multi-vision defect sensing system, lining composed primarily of IRT and RGB cameras, allows for automatic identification and extraction of small surface ailments, greatly enhancing detection efficiency. However, the presence of various issues like train vibration, inconsistent lighting, fluctuations in temperature and humidity leads to the images showing inadequate uniformity in illumination, blurriness, and a decrease in the level of detail. The above issues have led to unsatisfactory fusion processing results for multiple visual images and increased missed detection rates. To address the above-mentioned issue, multi visual images fusion approach for subway tunnel defects based on saliency optimization of pixel level defect image features is proposed. The approach initially analyses the train’s motion status and image blurring conditions. It then eliminates the dynamic blurring in the image. Secondly, Image weights are allocated based on the uniformity of visible light image illumination in the tunnel, as well as real-time temperature and humidity. Finally, image feature extraction and fusion are performed by a U-Net network that integrates channel attention mechanisms. The entire experiment was carried out on a dataset consisting of leakage data from the tunnel lining of Shanghai Metro and tunnel defect data from Beijing Metro. The experimental results demonstrate that this approach improves the image pixel value variation rate by 39.7%, enhances the edge quality by 23%, and outperforms similar approach in terms of average gradient, gradient quality, and sum of difference correlation with improvements of 15.9%, 7.3%, and 26.6% respectively.