ABSTRACT The rapid growth of air transportation has surpassed the capabilities of traditional airport surveillance methods, such as visual observation and auxiliary equipment (e.g., ADS‐B, MLAT, radar), which struggle to provide all‐area, all‐weather situation awareness. Vision‐based deep learning methods, being cost‐effective and scalable, present promising alternatives but often fall short in delivering comprehensive awareness. Multi‐task learning (MTL) addresses these gaps by enabling models to simultaneously learn multiple related tasks, improving overall perception and decision‐making. Thus, this review reviews MTL systems for airport surface surveillance, categorising tasks into scene perception for intensive estimation and monitoring for non‐intensive estimation. This review identifies three key challenges: (1) efficient information sharing across tasks, (2) balancing multiple tasks, and (3) enhancing model training efficiency. The review examines these challenges through three lenses: neural network architecture, loss function optimization, and learning paradigms, proposing optimization strategies for each. As the first review to focus on MTL in airport surveillance, this review provides valuable insights into model design, task balancing, and training strategies, offering guidance for the future development of intelligent airport monitoring systems.

In this paper, we propose an iterative framework, which consists of two phases: a generation phase and a training phase, to generate realistic training data for supervised small-baseline and large-baseline homography learning and yield a state-of-the-art homography estimation network. In the generation phase, given an unlabeled image pair, we utilize the pre-estimated dominant plane masks and homography of the pair, along with another sampled homography that serves as ground truth to generate a new labeled training pair with realistic motion. In the training phase, the generated data is used to train the supervised homography network, in which the training data is refined via a content refinement diffusion model. Once an iteration is finished, the trained network is used in the next data generation phase to update the pre-estimated homography. Through such an iterative strategy, the quality of the dataset and the performance of the network can be gradually and simultaneously improved. Experimental results show that our method outperforms existing competitors and previous supervised methods can also be improved based on the generated dataset.

In this letter, we propose a diffusion-based framework that leverages the generative ability of diffusion models and the advantages of the physically explainable Fourier transformation for visually satisfactory low-light image enhancement. Specifically, we first employ an encoder to convert the paired low-light and normal-light images into latent features and transform the features into the frequency domain through Fourier transformation, resulting in amplitude components that contain illumination information and phase components that represent details information. Subsequently, we present the latent-Fourier diffusion model which performs diffusion operations on the phase components for details reconstruction. Furthermore, we propose a lightness boost module to reconstruct amplitude aiming to improve the contrast in the frequency domain, and the restored feature obtained by performing inverse Fourier transformation on the reconstructed phase and amplitude components is further refined by the proposed latent feature fusion module to achieve better visual perception. Finally, the refined feature is taken as input to a decoder to produce the final restored image. Extensive experiments on publicly available benchmarks demonstrate our proposed method outperforms state-of-the-art competitors.

Futuristic Space Networks (SN) present unprece-dented prospects for ubiquitous, low-latency Internet services. Yet, these networks also encounter unique challenges arising from the dynamic nature of satellites on a global scale. To comprehensively address emerging issues in SNs, researchers require the capability to conduct a diverse array of experiments. However, existing experimental approaches either implement visualization functionality but lack network functionality (e.g., the space simulator), or implement network functionality but lack visualization (e.g., the networking simulator). In this paper, we present SNK, a novel simulation platform with visualization and networking capabilities for evaluating the space network performance of global Internet services. SNK offers real-time communication visualization and supports the simulation of routing between edge node of network. The platform enables the evaluation of routing and network performance metrics such as latency, stretch, network capacity, and throughput under different network structures and density. The effectiveness of SNK is demonstrated through various simulation cases, including the routing between fixed edge stations or mobile edge stations and analysis of snace network structures.

Homography estimation is erroneous in the case of large-baseline due to the low image overlay and limited receptive field. To address it, we propose a progressive estimation strategy by converting large-baseline homography into multiple intermediate ones, cumulatively multiplying these intermediate items can reconstruct the initial homography. Meanwhile, a semi-supervised homography identity loss, which consists of two components: a supervised objective and an unsupervised objective, is introduced. The first supervised loss is acting to optimize intermediate homographies, while the second unsupervised one helps to estimate a large-baseline homography without photometric losses. To validate our method, we propose a large-scale dataset that covers regular and challenging scenes. Experiments show that our method achieves state-of-the-art performance in large-baseline scenes while keeping competitive performance in small-baseline scenes. Code and dataset are available at https://github.com/megvii-research/LBHomo.

The use of a mega-constellation comprised of thousands of Low Earth Orbit (LEO) satellites for global internet service has garnered significant attention. In the network layer of this system, geographical routing has been found to outperform centralized strategies due to the lower complexity and overhead. However, geographical routing can still result in “dead-ends” due to network gaps. To address this issue, we propose the use of encountering inter-satellite links (eISLs) to improve network connectivity and routing reachability. We further present a system model and analysis of eISLs, as well as our Dynamic eISLs Configuration (DeC) algorithm for establishing eISLs between encountering satellites. Our experimental results demonstrate that our proposed DeC under eISLs enabling in satellite networks can significantly reduce propagation latency by 22% and path stretch by 15% in centralized routing algorithms. Moreover, in geographical routing, DeC can effectively improve the reachable ratio from 55% to 100% while maintaining a 28% increase in throughput, outperforming schemes without eISLs. Our proposed eISL-enabled satellite network architecture shows promising results in improving routing efficiency and connectivity in LEO satellite systems.

In this paper, we propose an iterative framework, which consists of two phases: a generation phase and a training phase, to generate realistic training data and yield a supervised homography network. In the generation phase, given an unlabeled image pair, we utilize the pre-estimated dominant plane masks and homography of the pair, along with another sampled homography that serves as ground truth to generate a new labeled training pair with realistic motion. In the training phase, the generated data is used to train the supervised homography network, in which the training data is refined via a content consistency module and a quality assessment module. Once an iteration is finished, the trained network is used in the next data generation phase to update the pre-estimated homography. Through such an iterative strategy, the quality of the dataset and the performance of the network can be gradually and simultaneously improved. Experimental results show that our method achieves state-of-the-art performance and existing supervised methods can be also improved based on the generated dataset. Code and dataset are available at https://github.com/JianghaiSCU/RealSH.

Similarity measure of time series with different sampling frequencies is vitally important for many signal processing applications. Dynamic Time Warping (DTW) is one of the most popular methods for similarity measure of time series. However, conventional DTW algorithms have limitations when dealing with time series of different sampling frequencies due to context density inconsistency such as different internal change frequencies of derivatives, shapes, events and distances in local neighborhoods. In light of this, we propose a novel Context Density Consistency Dynamic Time Warping (CDC-DTW) algorithm. It firstly designs local context windows adaptive to the lengths of time series. Then it proposed a local spatial-temporal context density consistency technique by down-sampling and interpolation compensating the high-frequency time series following the context density of low-frequency time series. Besides, a normalized Hamming window weighting function is embedded into the local contexts to create robust weighted cost measure. Extensive experimental results on 128 gold-standard UCR datasets showed that CDC-DTW increased the similarity measure accuracy by 70.53% in average comparing with other 6 classic and state-of-the-art DTW baseline algorithms.

The take-off and landing activities of aircraft must operate daily in a safe and orderly manner to provide a safe and efficient transition of passengers and goods. The 6D pose estimation of the aircraft is the key to guaranteeing the safe take-off and landing of the aircraft. The vision-based 6D pose estimation method, an important method when GPS and gyroscope are unavailable, faces the problem of poor estimation accuracy due to large scenes and large depths. An end-to-end 6D pose estimation method of the aircraft is proposed to solve this problem. Firstly, this paper combines the rigid structure characteristic of the aircraft with the direction property of arrows to build an aircraft 3D skeleton with reconstruction ability, simplicity, and direction properties. Secondly, this paper reconstructs the predesigned 3D skeleton of the aircraft from an RGB image and explores the 6D pose information in the reconstructed 3D skeleton. A 3D matrix is used to show the 3D skeleton and improve the encoding of the spatial information in the 3D skeleton. The experimental results show that the proposed method outperforms Wide-Depth-Range by 199% and 105% on the metric ADD and Rete, respectively. Compared with YOLO6D, the proposed method is 58.9% faster.

The skeleton shows the local symmetry and the shape/topology of the object, and it is utilized for human pose recognition, road detection, text detection, and the representation of industrial parts. However, the size of the skeleton is variable, which makes high-level feature representation difficult. Existing methods only attempt to integrate multilevel features but ignore the extraction of high-level features and multiscales of contextual information that are helpful for the skeleton detection task. Thus, the contributions of this article include two aspects. The first contribution is to propose a nested densely atrous spatial pyramid pooling method that connects the atrous convolutions with different dilation rates in a nested cascade mode, which can provide the multiscale denser contextual information, a larger receptive field, and more local features. The second one is to propose a deep dense short connection (DDSC) that explores the role of features at different levels in the task of skeleton detection. DDSC adopts concatenation to fuse high-level semantic information with shape information. The proposed method is evaluated on four common datasets, and the experimental results show the effectiveness of the proposed method.

Diffusion models have achieved promising results in image restoration tasks, yet suffer from time-consuming, excessive computational resource consumption, and unstable restoration. To address these issues, we propose a robust and efficient Diffusion-based Low-Light image enhancement approach, dubbed DiffLL. Specifically, we present a wavelet-based conditional diffusion model (WCDM) that leverages the generative power of diffusion models to produce results with satisfactory perceptual fidelity. Additionally, it also takes advantage of the strengths of wavelet transformation to greatly accelerate inference and reduce computational resource usage without sacrificing information. To avoid chaotic content and diversity, we perform both forward diffusion and denoising in the training phase of WCDM, enabling the model to achieve stable denoising and reduce randomness during inference. Moreover, we further design a high-frequency restoration module (HFRM) that utilizes the vertical and horizontal details of the image to complement the diagonal information for better fine-grained restoration. Extensive experiments on publicly available real-world benchmarks demonstrate that our method outperforms the existing state-of-the-art methods both quantitatively and visually, and it achieves remarkable improvements in efficiency compared to previous diffusion-based methods. In addition, we empirically show that the application for low-light face detection also reveals the latent practical values of our method. Code is available at https://github.com/JianghaiSCU/Diffusion-Low-Light.

Abstract Video‐based person re‐identification (Re‐ID) has received increasing attention in video surveillance analysis in recent years. To extract relevant information of the target, many existing methods utilise the attention mechanism in the residual block of the ResNet. However, these methods only focus on the residual block and ignore the output of the shortcut part, which also contains rich information about the person. To solve this problem, a different aspect of network design is investigated: the insert position of the attention module. To simultaneously explore the discriminative information in both the residual block and the shortcut, a novel multi‐stage attention method is proposed by inserting the attention mechanism between stages of ResNet. Using this method can effectively extract the rich discriminative features of the target to better distinguish different pedestrians and improve the feature extraction capabilities of the model. Extensive experiments are conducted on four popular video‐based person Re‐ID datasets to demonstrate the effectiveness of the authors’ proposed method and display its superiority with the existing video‐based person Re‐ID methods.

This paper aims to combine spatial and frequency information for single image deblurring. Although some methods have tried to use frequency information to perform deblurring, they only simply process the different frequencies information separately or concatenate the real part and imaginary part of frequency features but ignore the strong correlation between them. To address this problem, we propose a simple but effective frequency interaction pipeline to realize the mutual conversion of the real part and the imaginary part. Then, we construct a spatial-frequency conversion module (SFCM) to promote the mutual conversion between the frequency information and the spatial information. Based on the proposed components, we build a multi-scale deblurring network, dubbed SFDNet, which can fully exploit coarse and middle-level information in spatial and frequency domains for finer scale image deblurring. Extensive experiments on the GoPro and HIDE datasets demonstrate that the proposed network outperforms the state-of-the-art methods both quantitatively and visually.

In this work, we simulate the early visual information processing mechanisms in biological visual system (BVS) to solve the visible-infrared image fusion (VIIF) task. Concretely, both infrared and visible images are first processed with a dynamic receptive field (DRF), which is imitated by a Difference of Gaussian (DoG) function whose parameters are adjusted according to local image statistics (e.g., local edge responses). The DRF processing produces two components for each source image (e.g., the visible image or the infrared image) that respectively represent the results of On-center based DRF and Off-center based DRF. Then, the results of On-center based DRF for visible image are fused with the results of Off-center based DRF for infrared image and the results of On-center based DRF for infrared image are fused with the results of Off-center based DRF for visible image, according to the mechanisms of cortex-based center-surround fusion. Algorithmically, this step fuses the visible and infrared images processed by DRF with On-center and Off-center according to the different levels of local homogeneity. Moreover, a feedward signal acting as the sub-cortical flow is also introduced to adjust the results during the fusion. The final output image is simply obtained by the summation of two components after the cortex and sub-cortex based fusion. Qualitative and quantitative tests on four datasets demonstrate that our approach can fuse the infrared and visible images effectively with the good background details and discernible salient areas. We emphasize the importance of corticothalamic feedback, cortex and sub-cortex-based fusion, and the interactions between On-center pathway and Off-center pathway that are ubiquitous in BVS for producing the high-quality visual signals.

To deal with the impact of emergent events and keep air traffic control (ATC) equipment operation, planning and expansion research of ATC equipment has become an urgent problem. In this paper, the network model is constructed based on the route structure of navigation station in the ATC. Using maximal connected subset, network efficiency, node vulnerability, maintenance efficiency fusing flight flow, we evaluate the vulnerability of navigation station equipment from the perspective of network structure and network combined with physical condition. By analyzing the network in Southwest China, we find that random attacks lead to lowest vulnerability. Navigation station equipment network is a scale-free network. Node betweenness attacks cause the network efficiency changing rapidly. The vulnerability assessment value of the navigation station node integrated with the contribution of adjacent navigation station nodes follows the power-law distribution. Unreasonable distributions of equipment lead to some equipment redundancy. At the same time, we can also balance the distribution of network traffic load to improve reliability and sustainability of network. Considering the actual flow load, changing the flight flow can ensure the control of the working load of navigation station nodes in the limit load and reduce the vulnerability of navigation station equipment network. These methods provide a new theoretical basis for planning and expansion of navigation station equipment network, greatly reduce the impact of unexpected events and maintain the stable operation of civil aviation.

The time-band approximation model on flight operations recovery problems is constructed based on the time-band network, and the object of its' mathematical representation can capture approximated delay costs and cancellation costs. But the calculation of delay costs associated with the actual flying time in China, the actual flying time should not be a fixed time but a random time around the planned flying time. In this paper, we describe the approximated delay costs considering the random flying time around the planned flying time. Subsequently, we give out the time-band approximation model on flight operations recovery with the random flying time. Finally, we design a set of practical numerical experiments. The results demonstrate that the time-band approximation model on flight operations recovery with the random flying time may be more efficient than the time band approximation model on flight operations recovery with the planned flying time.