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Visible-Thermal Tiny Object Detection: A Benchmark Dataset and Baselines
Xinyi Ying;Chao Xiao;Wei An;Ruojing Li;Xu He;Boyang Li;Xu Cao;Zhaoxu Li;Yingqian Wang;Mingyuan Hu;Qingyu Xu;Zaiping Lin;Miao Li;Shilin Zhou;Weidong Sheng;Li Liu
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year: 2025 | Early Access Article |
Cited by: Papers (1)
Visible-thermal small object detection (RGBT SOD) is a significant yet challenging task with a wide range of applications, including video surveillance, traffic monitoring, search and rescue. However, existing studies mainly focus on either visible or thermal modality, while RGBT SOD is rarely explored. Although some RGBT datasets have been developed, the insufficient quantity, limited diversity, ...Show More
Sparsity-Aware Global Channel Pruning for Infrared Small-Target Detection Networks
For infrared small-target detection, convolutional neural network (CNN)-based methods have demonstrated promising performance. However, due to the small size of the targets, existing infrared small-target detection methods necessitate intricate structures with intensive computation to maintain distinctive features of targets in deep layers, which poses great challenges for deployment on edge devic...Show More
Direction-Coded Temporal U-Shape Module for Multiframe Infrared Small Target Detection
Ruojing Li;Wei An;Chao Xiao;Boyang Li;Yingqian Wang;Miao Li;Yulan Guo
Infrared small target (IRST) detection aims at separating targets from cluttered background. Although many deep learning-based single-frame IRST (SIRST) detection methods have achieved promising detection performance, they cannot deal with extremely dim targets while suppressing the clutters since the targets are spatially indistinctive. Multiframe IRST (MIRST) detection can well handle this probl...Show More
Highly Efficient and Unsupervised Framework for Moving Object Detection in Satellite Videos
Chao Xiao;Wei An;Yifan Zhang;Zhuo Su;Miao Li;Weidong Sheng;Matti Pietikäinen;Li Liu
Moving object detection in satellite videos (SVMOD) is a challenging task due to the extremely dim and small target characteristics. Current learning-based methods extract spatio-temporal information from multi-frame dense representation with labor-intensive manual labels to tackle SVMOD, which needs high annotation costs and contains tremendous computational redundancy due to the severe imbalance...Show More
Heterogeneous Graph Transformer for Multiple Tiny Object Tracking in RGB-T Videos
Qingyu Xu;Longguang Wang;Weidong Sheng;Yingqian Wang;Chao Xiao;Chao Ma;Wei An
Tracking multiple tiny objects is highly challenging due to their weak appearance and limited features. Existing multi-object tracking algorithms generally focus on single-modality scenes, and overlook the complementary characteristics of tiny objects captured by multiple remote sensors. To enhance tracking performance by integrating complementary information from multiple sources, we propose a no...Show More
DiffDet4SAR: Diffusion-Based Aircraft Target Detection Network for SAR Images
Jie Zhou;Chao Xiao;Bo Peng;Zhen Liu;Li Liu;Yongxiang Liu;Xiang Li
Aircraft target detection in synthetic aperture radar (SAR) images is a challenging task due to the discrete scattering points and severe background clutter interference. Currently, methods with convolution- or transformer-based paradigms cannot adequately address these issues. In this letter, we explore diffusion models for SAR image aircraft target detection for the first time and propose a nove...Show More
RepISD-Net: Learning Efficient Infrared Small-Target Detection Network via Structural Re-Parameterization
Shuanglin Wu;Chao Xiao;Longguang Wang;Yingqian Wang;Jungang Yang;Wei An
Infrared small-target detection is a challenging task for deep learning-based methods, because targets tend to disappear in the deep layers. To handle this problem, the existing deep neural networks usually apply various dense and skip connections for feature maintenance. Although these well-designed networks have achieved good detection performance, the complex network structures reduce their eff...Show More
You Only Train Once: Learning a General Anomaly Enhancement Network With Random Masks for Hyperspectral Anomaly Detection
Zhaoxu Li;Yingqian Wang;Chao Xiao;Qiang Ling;Zaiping Lin;Wei An
In this article, we introduce a new approach to address the challenge of generalization in hyperspectral anomaly detection (AD). Our method eliminates the need for adjusting parameters or retraining on new test scenes as required by most existing methods. Employing an image-level training paradigm, we achieve a general anomaly enhancement network for hyperspectral AD that only needs to be trained ...Show More
Incorporating Deep Background Prior Into Model-Based Method for Unsupervised Moving Vehicle Detection in Satellite Videos
Chao Xiao;Ting Liu;Xinyi Ying;Yingqian Wang;Miao Li;Li Liu;Wei An;Zhijie Chen
Background reconstruction is a key step of moving object detection in satellite videos. Most existing model-based methods exploit low-rank prior to recover background, which has achieved good performance but suffered degradation under complex and dynamic scenes. In this article, we introduce a deep background prior into model-based methods for moving vehicle detection in satellite videos. Our deep...Show More
MTU-Net: Multilevel TransUNet for Space-Based Infrared Tiny Ship Detection
Tianhao Wu;Boyang Li;Yihang Luo;Yingqian Wang;Chao Xiao;Ting Liu;Jungang Yang;Wei An;Yulan Guo
Space-based infrared tiny ship detection aims at separating tiny ships from the images captured by Earth-orbiting satellites. Due to the extremely large image coverage area (e.g., thousands of square kilometers), candidate targets in these images are much smaller, dimer, and more changeable than those targets observed by aerial- and land-based imaging devices. Existing short imaging distance-based...Show More
Dense Nested Attention Network for Infrared Small Target Detection
Boyang Li;Chao Xiao;Longguang Wang;Yingqian Wang;Zaiping Lin;Miao Li;Wei An;Yulan Guo
Single-frame infrared small target (SIRST) detection aims at separating small targets from clutter backgrounds. With the advances of deep learning, CNN-based methods have yielded promising results in generic object detection due to their powerful modeling capability. However, existing CNN-based methods cannot be directly applied to infrared small targets since pooling layers in their networks coul...Show More
The First Challenge on Moving Object Detection and Tracking in Satellite Videos: Methods and Results
Yulan Guo;Qian Yin;Qingyong Hu;Feng Zhang;Chao Xiao;Ye Zhang;Hanyun Wang;Chenguang Dai;Jian Yang;Zhuang Zhou;Weilong Guo;Xiyu Qi;Kelong Tu;Cong Xu;Shudan Zhu;Lai Chen;Bin Lin;Chaocan Xue;Jinlei Zheng;Limei Qin;Ying Li;Manqi Zhao;Lu Ruan;Mingpeng Cui;Guanchen Ding;Guangwei Jiang;Zhenzhong Chen;Yuhan Sun;Kaiyang Cao;Lingyu Kong;Shaodong Chen;Zhicheng Zhao;Qin Shen;Lei Liu;Chenglong Li;Yun Xiao
2022 26th International Conference on Pattern Recognition (ICPR)
Year: 2022 | Conference Paper |
Cited by: Papers (5)
In this paper, we briefly summarize the first challenge on moving object detection and tracking in satellite videos (SatVideoDT). This challenge has three tracks related to satellite video analysis, including moving object detection (Track 1), single object tracking (Track 2), and multiple-object tracking (Track 3). 123, 89, and 70 participants successfully registered, while 37, 42, and 29 teams s...Show More
The First Challenge on Moving Object Detection and Tracking in Satellite Videos: Methods and Results
Yulan Guo;Qian Yin;Qingyong Hu;Feng Zhang;Chao Xiao;Ye Zhang;Hanyun Wang;Chenguang Dai;Jian Yang;Zhuang Zhou;Weilong Guo;Xiyu Qi;Kelong Tu;Cong Xu;Shudan Zhu;Lai Chen;Bin Lin;Chaocan Xue;Jinlei Zheng;Limei Qin;Ying Li;Manqi Zhao;Lu Ruan;Mingpeng Cui;Guanchen Ding;Guangwei Jiang;Zhenzhong Chen;Yuhan Sun;Kaiyang Cao;Lingyu Kong;Shaodong Chen;Zhicheng Zhao;Qin Shen;Lei Liu;Chenglong Li;Yun Xiao
Year: 2022 | Conference Paper |
Video Infrared Small Target Detection Combining Hybrid Attention with Cross-Scale Feature Fusion
Zaiping Lin;Yihang Luo;Qiang Ling;Tianhao Wu;Chao Xiao;Boyang Li
2022 2nd International Conference on Frontiers of Electronics, Information and Computation Technologies (ICFEICT)
Year: 2022 | Conference Paper |
Cited by: Papers (2)
Infrared small target detection is widely used in marine resource utilization, high-precision navigation, ecological environment monitoring, and other fields. Traditional detection methods rely on manual feature templates and prior knowledge, which are not robust in complex scenarios. Although achieving promising results, existing deep learning-based methods mainly focus on single-frame target det...Show More
RSMOT: Remote Sensing Multi-Object Tracking Network with Local Motion Prior for Objects in Satellite Videos
Chao Xiao;Shuanglin Wu;Yingqian Wang;Miao Li;Wei An;Zhijie Chen
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
Year: 2022 | Conference Paper |
Cited by: Papers (3)
Multi-object tracking (MOT) in satellite videos is a new and challenging task. The difficulties stem from the extremely small objects and the low contrast between objects and background. To tackle the challenges of MOT in satellite videos, a multi-object tracking method is proposed in this paper to incorporate the local motion prior into the network. Specifically, we design a local cost volume con...Show More
Nonconvex Tensor Low-Rank Approximation for Infrared Small Target Detection
Ting Liu;Jungang Yang;Boyang Li;Chao Xiao;Yang Sun;Yingqian Wang;Wei An
Infrared small target detection is an important fundamental task in the infrared system. Therefore, many infrared small target detection methods have been proposed, in which the low-rank model has been used as a powerful tool. However, most low-rank-based methods assign the same weights for different singular values, which will lead to inaccurate background estimation. Considering that different s...Show More
DSFNet: Dynamic and Static Fusion Network for Moving Object Detection in Satellite Videos
Chao Xiao;Qian Yin;Xinyi Ying;Ruojing Li;Shuanglin Wu;Miao Li;Li Liu;Wei An;Zhijie Chen
Moving object detection (MOD) in satellite videos remains challenging due to the extremely small size of the interested targets and the highly complex background. Both the intra-frame (static) and inter-frame (dynamic) information are of great importance to MOD. In this letter, we propose a two-stream detection network named dynamic and static fusion network (DSFNet) to tackle the MOD problem in s...Show More
Dense Dual-Attention Network for Light Field Image Super-Resolution
Light field (LF) images can be used to improve the performance of image super-resolution (SR) because both angular and spatial information is available. It is challenging to incorporate distinctive information from different views for LF image SR. Moreover, the long-term information from the previous layers can be weakened as the depth of network increases. In this paper, we propose a dense dual-a...Show More
Baseline-Adaptive Light Field Depth Estimation Based on Stereo Matching
2021 6th International Symposium on Computer and Information Processing Technology (ISCIPT)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
Using the spatial and angular information contained in a light field, the depth map can be generated and the structure of the scene can be inferred. However, it is always challenging to obtain an accurate and robust depth map in different scenes with different resolutions and baselines. In this paper, we propose a resolution and baseline adaptive light field depth estimation method to increase the...Show More
Camera Array Calibration Using a Simple Checkerboard Pattern
2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP)
Year: 2019 | Conference Paper |
Traditional camera calibration method usually considers a single camera or dual cameras. As the growing development of imaging technology, multi camera and camera array are used in more and more applications. In this paper, a calibration method for camera array is proposed. The intrinsic and extrinsic parameters for an individual camera are obtained using single camera calibration method. Then the...Show More
Producing depth maps of a scene is essential in many light field applications. Numerous algorithms have been developed to obtain an accurate depth map on public datasets. However, real-world light field images obtained by cameras have noises produced by hardware limitations. Existing methods do not perform well on noisy scenes. In this paper, we propose a noise-aware light field depth estimation a...Show More
Selective Light Field Refocusing for Camera Arrays Using Bokeh Rendering and Superresolution
IEEE Signal Processing Letters
Cited by: Papers (94)
Camera arrays provide spatial and angular information within a single snapshot. With refocusing methods, focal planes can be altered after exposure. In this letter, we propose a light field refocusing method to improve the imaging quality of camera arrays. In our method, the disparity is first estimated. Then, the unfocused region (bokeh) is rendered by using a depth-based anisotropic filter. Fina...Show More
Light field cameras become increasingly popular recently, as they can capture 3-D geometry of the scene in a single snap-shot. Many post-capture adjustments can be realized after the disparity map or the equivalent depth map being estimated. Recent studies about light field depth recovery are more designed for commercial microlens cameras, such as Lytro and Raytrix. However, camera arrays capture ...Show More
Multi-image superresolution (SR) techniques produce a high-resolution image from several low-resolution observations. Previous reconstruction-based SR approaches focus more on the optimization models but have not adequately emphasized the mathematic-solving techniques for this typically ill-conditioned and under-determined large scale problem. Since step size plays an important role in the iterati...Show More
Flexible Refocusing Method for Camera Arrays Based on Reprojection
2017 International Conference on Computer Technology, Electronics and Communication (ICCTEC)
Year: 2017 | Conference Paper |
Light field provides enough information so that one can post-process images after acquisition. A representative application of light-field is refocusing, which changes the infocus region of images. In this paper, a flexible algorithm of refocusing is proposed for images captured by camera arrays. By utilizing the parameters estimated by camera calibration, the reprojection can be used to produce i...Show More
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