The Yutu 2 lunar rover has been engaged in scientific research on the lunar surface for more than 3 years. In order to maximize the efficiency of planning and driving for the Yutu 2, the distance between the beginning and end stations of each movement is large. This makes significant scale differences and complex affine transformations between the images in adjacent stations, which usually causes failure of image matching and navigation. To address the above problems, current methods use the single-dimensional pixel spatial resolution model based on imaging geometry to analyze the reasons of failed or successful images matching. However, they are not adaptable to the images with complex affine transformation. In this paper, a general two-dimensional pixel spatial resolution model based on the algebraic derivation of homography transformation is proposed. With the proposed model, the scale difference between images of adjacent stations can be quantitative analyzed, and rate of successful matching between images which have complex affine transformation and scale differences is increased. This will benefit the engineering application such as visual localization and navigation. Experiments under various conditions such as the horizontal and inclined lunar surface are performed to verify the effectiveness of the proposed method.

With the advancement of human exploration of near-Earth space, the number of space debris in Earth's orbit has increased dramatically, posing serious threats to orbiting spacecraft. Traditional space debris detection methods exhibit significant limitations in handling small-scale targets, complex backgrounds, and real-time requirements. This paper proposes the Space Debris Detection Transformer (SD-DETR) algorithm, an end-to-end detector specifically optimized for space debris detection tasks. First, we design a Dynamic Convolutional Network backbone that enables adaptive feature extraction for multi-scale, irregularly shaped debris through the dynamic convolution mixing mechanism of the Adaptive Multi-scale Feature Block. Second, we propose a Cross-scale Collaborative Attention Fusion Module that realizes deep feature fusion through hybrid domain feature enhancement and channel-guided spatial attention mechanisms. Finally, we introduce a Multi-Level Adaptive Feature Bridging module that enhances cross-hierarchical feature integration through dual-scale spatial perception attention. On the constructed Nebula space debris dataset, SD-DETR achieves 89.4% mAP50 and 75.4% mAP50-95, improving by 2.1% and 2.9% respectively compared to the baseline model. Generalization experiments on the NCSTP-10000 dataset further validate the effectiveness of the method, demonstrating its superior detection performance in complex space environments.

In this letter, we propose a novel lightweight X-ray image contraband segmentation network, XSNet, which integrates State Space Models (SSM) with Convolutional Neural Networks (CNNs) to achieve a significant trade-off between segmentation accuracy and lightweight design for computer-aided X-ray security check. The model is built based on the encoder-decoder framework. Specifically, we design an Multi-scale Convolution Fusion (MCF) block for multi-scale information extraction and a Dual-branch State Space Model (DSSM) block to relieve the bias caused by the imbalance of single branch structure in feature extraction and maintain the capabilities of SSM in modeling long range pixel dependencies. In addition, we present two versions of the model in two different sizes called XSNet-s and XSNet-l respectively. The quantitative and qualitative evaluations on the public PIDray and PIXray datasets both show the superiority of two models in terms of mean Intersection over Union (mIoU) and FLOPs.