Local feature description is crucial for robotic tasks, yet existing methods struggle with motion blur, a prevalent challenge in high-dynamic and low-light environments. While effective on sharp images, they suffer significant degradation under blur. To address this issue, we propose Motion-Feat, an end-to-end motion blur-aware feature description method. Our approach introduces a Motion Deformable Block (MDB) that adaptively adjusts the receptive field based on pixel-wise motion information at different stages of the network, enhancing multi-scale feature descriptor robustness in blurred conditions. Additionally, we construct synthetic blurred datasets to systematically benchmark feature matching performance across varying blur intensities. Extensive experiments demonstrate that Motion-Feat outperforms state-of-the-art methods on blurred images while maintaining competitive performance on sharp images for relative camera pose estimation and homography estimation tasks. Both code and datasets are available at https://github.com/AndreGao08/Motion-Feat.

Cross-view geo-localization provides an offline visual positioning strategy for unmanned aerial vehicles (UAVs) in Global Navigation Satellite System (GNSS)-denied environments. However, it still faces the following challenges, leading to suboptimal localization performance: 1) Existing methods primarily focus on extracting global features or local features by partitioning feature maps, neglecting the exploration of spatial information, which is essential for extracting consistent feature representations and aligning images of identical targets across different views. 2) Cross-view geo-localization encounters the challenge of data imbalance between UAV and satellite images. To address these challenges, the Spatial Hybrid Attention Network with Adaptive Cross-Entropy Loss Function (SHAA) is proposed. To tackle the first issue, the Spatial Hybrid Attention (SHA) method employs a Spatial Shift-MLP (SSM) to focus on the spatial geometric correspondences in feature maps across different views, extracting both global features and fine-grained features. Additionally, the SHA method utilizes a Hybrid Attention (HA) mechanism to enhance feature extraction diversity and robustness by capturing interactions between spatial and channel dimensions, thereby extracting consistent cross-view features and aligning images. For the second challenge, the Adaptive Cross-Entropy (ACE) loss function incorporates adaptive weights to emphasize hard samples, alleviating data imbalance issues and improving training effectiveness. Extensive experiments on widely recognized benchmarks, including University-1652, SUES-200, and DenseUAV, demonstrate that SHAA achieves state-of-the-art performance, outperforming existing methods by over 3.92%.

Visual Place Recognition (VPR) techniques commonly utilize Contrastive Losses (CL) to train models that generate compact and discriminative global descriptors for images. These models often result in poor performance due to one of the following reasons during training: 1) loss functions that focus primarily on easier samples, 2) reliance on time-consuming hard sample mining methods to identify informative supervisory samples, which hinders effective learning from large-scale datasets. To enhance both learning efficiency and effectiveness, we propose a Curricular Contrastive Loss (CCL) and use graded similarity labels as a measure of sample difficulty. Inspired by human learning that begin with easier concepts and progressively tackle more challenging ones, our CCL dynamically emphasizes easier samples during the initial training stages to achieve rapid convergence. The learning gradually focuses on harder samples in later training stages to bolster robustness of the models under challenging conditions. Our proposed method has been extensively evaluated on popular datasets, and the results demonstrate its superior performance compared to the CL and Generalized CL functions.

Visual Place Recognition (VPR) utilizing patch descriptors from Convolutional Neural Networks (CNNs) has shown impressive performance in recent years. Existing works either perform exhaustive matching of all patch descriptors, or employ complex networks to select good candidate patches for further geometric verification. In this work, we develop a novel two-step training-free patch selection method that is fast, while being robust to large occlusions and extreme viewpoint variations. In the first step, a self-attention mechanism is used to select sparse and evenly distributed discriminative patches in the query image. Next, a novel spatial-matching method is used to rapidly select corresponding patches with high similar appearances between the query and each reference image. The proposed method is inspired by how humans perform place recognition by first identifying prominent regions in the query image, and then relying on back-and-forth visual inspection of the query and reference image to attentively identify similar regions while ignoring dissimilar ones. Extensive experiment results show that our proposed method outperforms state-of-the-art (SOTA) methods in both place recognition precision and runtime, on various challenging conditions.

With the continuous development of network communication and Internet of Things technology, the smart factory of new energy vehicles is increasingly dependent on network communication technology. Due to its increasing openness, which leads to increasing security risks, the attackers' system vulnerability discovery ability and attack techniques are also improving, making the security threats of smart factories escalating. To improve the autonomous sensing and defence capability of smart production lines for security vulnerabilities in the collaborative manufacturing environment, we put forward an adaptive LDoS attack detection scheme based on RF-GMM algorithm in an SDN environment. The method distinguishes normal and abnormal states of networks in smart factories by establishing a multi-feature selection model and profiling network anomalies to achieve the detection for external intrusions.