The scale of targets in remote sensing images varies greatly and diversity. It has many small targets which distribute densely, and high complexity of image background. The number of network model parameters and the computation amount of the object detection algorithms based on deep learning is huge. It is difficult to apply them on the platform with fixed performance and limited computing resources. A lightweight remote sensing object detection model is proposed in this paper, which called Attention and Multi-Scale Feature Fusion Lightweight-YOLO (AMFLW-YOLO). The deep separable convolution, inverted residual, and linear bottleneck structure are employed to replace the standard convolution layer to reduce the model parameters in the backbone network of the model. The Coordinate Attention (CA) mechanism is introduced into the feature fusion network to capture the direction-aware and location-aware information across channels at the same time, which improves the accuracy of the network. The Bidirectional Feature Pyramid Network (BiFPN) structure is employed to strengthen feature extraction. The learnable weights are introduced to learn the importance of different input features. The multi-scale feature fusion is applied to improve the detection effect. Experimental results show that the algorithm achieves satisfactory performance in terms of efficiency and accuracy and has advantages in detection accuracy and model lightweight.

Cracks inside urban underground comprehensive pipe galleries are small and their characteristics are not obvious. Due to low lighting and large shadow areas, the differentiation between the cracks and background in an image is low. Most current semantic segmentation methods focus on overall segmentation and have a large perceptual range. However, for urban underground comprehensive pipe gallery crack segmentation tasks, it is difficult to pay attention to the detailed features of local edges to obtain accurate segmentation results. A Global Attention Segmentation Network (GA-SegNet) is proposed in this paper. The GA-SegNet is designed to perform semantic segmentation by incorporating global attention mechanisms. In order to perform precise pixel classification in the image, a residual separable convolution attention model is employed in an encoder to extract features at multiple scales. A global attention upsample model (GAM) is utilized in a decoder to enhance the connection between shallow-level features and deep abstract features, which could increase the attention of the network towards small cracks. By employing a balanced loss function, the contribution of crack pixels is increased while reducing the focus on background pixels in the overall loss. This approach aims to improve the segmentation accuracy of cracks. The comparative experimental results with other classic models show that the GA SegNet model proposed in this study has better segmentation performance and multiple evaluation indicators, and has advantages in segmentation accuracy and efficiency.

As the application of urban road extraction becomes more widespread, the challenges of segmentation errors and embedding large models into hardware become more complex. To solve these problems, an algorithm called attention mechanism and lightweight network fusion high-resolution network (AMLN-HRNet) is proposed. The network includes a lightweight convolution called deep sparse channel and spatial encoding convolution (DSCEConv) for road extraction, which greatly reduces the number of parameters in the model. To make the extraction of roads more accurate, an attention mechanism called lightweight dynamic weighted is designed. In addition, a parameterless attention mechanism is introduced to make the model properly combine the spatial correlation and topological structure of the road to improve extraction accuracy. Through experimental results, AMLN-HRNet can effectively balance the speed and accuracy of the model.

In canopy coverage monitoring, traditional segmentation models face limitations including limited modeling ability, insufficient generalization performance, and small target missed detection due to challenges such as fragmented canopy boundary fuzziness, spectral confusion from multi-layer shadows, and hyperspectral feature redundancy. We proposed quantum-enhanced U-Net to achieve fine canopy segmentation. The model adopts U-Net as the backbone network and introduces a quantum feature distillation mechanism together with a quantum feature mapping mechanism. These components form a hybrid quantum–classical architecture to capture global relationships and local details. In addition, the convolutional block attention module and a dynamic fusion strategy are incorporated for adaptive feature optimization. Experimental comparisons on the OpenAerialMap-Tree Canopy Dataset demonstrate the model’s superior performance. The unmanned aerial vehicle image dataset results demonstrate its strong cross-scenario generalization capability. Ablation experiments further validate the contributions of the quantum feature distillation module, quantum feature mapping module, and convolutional block attention module. Experimental outcomes indicate that this approach significantly enhances hyperspectral remote sensing canopy segmentation performance across multiple scenarios, demonstrating practical value in ecological monitoring.

To enhance the safety of deep-buried underground tunnel construction, it is crucial to monitor microseismic signals generated by rock micro-fractures. However, these signals are often contaminated by significant noise interference, which significantly hinders accurate signal interpretation. To address this challenge, this paper proposes an improved denoising method, Matching Pursuit Variational Mode Decomposition (MP-VMD) is aimed at effectively suppressing noise in microseismic signals. This paper proposes a MP-VMD algorithm that effectively eliminates noise in microseismic signals. Firstly, the MP algorithm is employed to sparse decompose the micro-seismic signal containing noise. Secondly, the Variational Mode Decomposition (VMD) algorithm is utilized to adaptively decompose the preliminary de-noised signal to obtain a series of modal components. Finally, the MP-VMD algorithm is combined with the correlation coefficient method to select the effective modal components to reconstruct the final denoised microseismic signal. The MP-VMD algorithm is applied to the denoising in laboratory simulation and to the denoising micro seismic signals from an underground engineering tunnel, and compare with wavelet decomposition, VMD, Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD) and Local Mean Decomposition (LMD) algorithm. The experimental results show that the MP-VMD algorithm can effectively remove the noise from microseismic signals. MP-VMD achieves a 15.2% higher signal noise ratio compared to wavelet decomposition while exhibiting a 23.7% reduction in root mean square error relative to conventional VMD.