Three-dimensional ultrasound (US) aims to provide sonographers with the spatial relationships of anatomical structures, playing a crucial role in clinical diagnosis. Recently, deep-learning-based freehand 3-D US has made significant advancements. It reconstructs volumes by estimating transformations between images without external tracking. However, image-only reconstruction poses difficulties in reducing cumulative drift and further improving reconstruction accuracy, particularly in scenarios involving complex motion trajectories. In this context, we propose an enhanced motion network (MoNetV2) to enhance the accuracy and generalizability of reconstruction under diverse scanning velocities and tactics. First, we propose a sensor-based temporal and multibranch structure (TMS) that fuses image and motion information from a velocity perspective to improve image-only reconstruction accuracy. Second, we devise an online multilevel consistency constraint (MCC) that exploits the inherent consistency of scans to handle various scanning velocities and tactics. This constraint exploits scan-level velocity consistency (SVC), path-level appearance consistency (PAC), and patch-level motion consistency (PMC) to supervise interframe transformation estimation. Third, we distill an online multimodal self-supervised strategy (MSS) that leverages the correlation between network estimation and motion information to further reduce cumulative errors. Extensive experiments clearly demonstrate that MoNetV2 surpasses existing methods in both reconstruction quality and generalizability performance across three large datasets.

With the increasing availability of cameras in vehicles, obtaining license plate (LP) information via on-board cameras has become feasible in traffic scenarios. LPs play a pivotal role in vehicle identification, making automatic LP detection (ALPD) a crucial area within traffic analysis. Recent advancements in deep learning have spurred a surge of studies in ALPD. However, the computational limitations of on-board devices hinder the performance of real-time ALPD systems for moving vehicles. Therefore, we propose a real-time frame-by-frame LP detector focusing on real-time accurate LP detection. Specifically, video frames are categorized into keyframes and non-keyframes. Keyframes are processed by a deeper network (high-level stream), while non-keyframes are handled by a lightweight network (low-level stream), significantly enhancing efficiency. To achieve accurate detection, we design a knowledge distillation strategy to boost the performance of low-level stream and a feature propagation method to introduce the temporal clues in video LP detection. Our contributions are: (1) A real-time frame-by-frame LP detector for video LP detection is proposed, achieving a competitive performance with popular one-stage LP detectors. (2) A simple feature-based knowledge distillation strategy is introduced to improve the low-level stream performance. (3) A spatial-temporal attention feature propagation method is designed to refine the features from non-keyframes guided by the memory features from keyframes, leveraging the inherent temporal correlation in videos. The ablation studies show the effectiveness of knowledge distillation strategy and feature propagation method.

As a unique identity of vehicle, License Plate (LP) facilitates the intelligent transportation in many fields, such as traffic enforcement, intelligent transportation dispatching, etc. Recently, the LP detectors are trained by supervised learning which is directly guided by manual annotations and lacks the use of visual knowledge in image content, limiting the further development of detection performance. Inspired by the contrast and comparison in perception of human beings, a contrastive learning method is introduced into license plate detection task and we propose an end-to-end Contrastive License Plate Detector (CLPD). In CLPD, a special contrastive triad for contrastive learning is designed which aims to decouple the foregrounds and backgrounds. Based on this triad, a contrastive learning branch is introduced into the license plate detection pipeline to prompt the feature expression ability of backbone and extracting more discriminative features for detection. This contrastive learning branch is jointly trained with supervised learning branch for detection and it is only used in training, keeping the efficiency in inference. The experiment results show that the proposed CLPD improves the detection accuracy compared to baselines and other license plate detectors significantly on three datasets. The ablation studies further explore the potential of CLPD. In addition, the proposed CLPD has generalization to improve the performance on different baselines. And the visualization results in latent space verify our proposed CLPD aggregates features tightly and extracts discriminative features effectively.

The Stable Diffusion model is a prominent text-to-image generation model that relies on a text prompt as its input, which is encoded using the Contrastive Language-Image Pre-Training (CLIP). However, text prompts have limitations when it comes to incorporating implicit information from reference images. Existing methods have attempted to address this limitation by employing expensive training procedures involving millions of training samples for image-to-image generation. In contrast, this paper demonstrates that the CLIP model, as utilized in Stable Diffusion, inherently possesses the ability to instantaneously convert images into text prompts. Such an image-to-prompt conversion can be achieved by utilizing a linear projection matrix that is calculated in a closed form. Moreover, the paper showcases that this capability can be further enhanced by either utilizing a small amount of similar-domain training data (approximately 100 images) or incorporating several online training steps (around 30 iterations) on the reference images. By leveraging these approaches, the proposed method offers a simple and flexible solution to bridge the gap between images and text prompts. This methodology can be applied to various tasks such as image variation and image editing, facilitating more effective and seamless interaction between images and textual prompts.

Scene Text Detection (STD) has applied in many fields successfully. One of the important applications of STD is License Plate Detection (LPD). As a unique identity of vehicle, License Plate (LP) facilitates the intelligent transportation in many fields, such as traffic enforcement, intelligent transportation dispatching, etc. However, there are many scene texts similar to LPs causing misjudgment of LP detector. To alleviate these disturbances, more discriminative features are necessary. In latent feature space, discriminative features should aggregate into a tight cluster to widen decision boundary. We assume three perspectives about how to aggregate features and boost feature expression. From these assumptions, a special contrastive triad is designed. Then, we propose a Self-Constrained Contrastive Aggregation (SCCA) method to lead the feature aggregation in latent space and boost the feature expression of backbone. The proposed SCCA is jointly trained with supervised learning for detection to improve the detection performance. The experiments show that our proposed SCCA prompts the baseline significantly and exceeds recent LP detectors, reaching 99.7 on both F1-score and AP on UFPR-ALPR dataset. Meanwhile, we compare the self-constrained contrastive learning with vanilla contrastive learning in experiments and visualize their LP features. The results show that our proposed SCCA reaches better performance and verifies our assumptions are reasonable.

DDoS attacks have emerged as one of the most serious network security threats in 5G, IoT, multi-cloud, and other emerging technology scenarios. The bandwidth of DDoS attacks is increasing in the new scenario, but the current network structure and security devices are inflexible. We propose a DDoS protection method based on SDN multi-dimensional scheduling method and DDoS scrubbing policy, which not only plans the scheduling path, but also blocks and redirects different kinds of attack traffic that used dynamic residual bandwidth of links, the number of flow entries in OpenFlow switches, and scheduling path length. To flexibly protect against DDoS attacks, this method combines scheduling and protection means. The experimental results indicate that the scheduling is effective. The scheduling, path produced by this method outperforms ECMP and KSP approaches in throughput, packet loss rate, and jitter, and it can block L3/L4 attack traffic and redirect L7 attack traffic.

With the great changes in network scale and network topology, the difficulty of DDoS attack detection increases significantly. Most of the methods proposed in the past rarely considered the real-time, adaptive ability, and other practical issues in the real-world network attack detection environment. In this paper, we proposed a real-time adaptive DDoS attack detection method RT-SAD, based on the response to the external network when attacked. We designed a feature extraction method based on sketch and an adaptive updating algorithm, which makes the method suitable for the high-speed network environment. Experiment results show that our method can detect DDoS attacks using sampled Netflowunder high-speed network environment, with good real-time performance, low resource consumption, and high detection accuracy.