Object detection using aerial drone imagery has received a great deal of attention in recent years. While visible light images are adequate for detecting objects in most scenarios, thermal cameras can extend the capabilities of object detection to night-time or occluded objects. As such, RGB and Infrared (IR) fusion methods for object detection are useful and important. One of the biggest challenges in applying deep learning methods to RGB/IR object detection is the lack of available training data for drone IR imagery, especially at night. In this paper, we develop several strategies for creating synthetic IR images using the AIRSim simulation engine and CycleGAN. Furthermore, we utilize an illumination-aware fusion framework to fuse RGB and IR images for object detection on the ground. We characterize and test our methods for both simulated and actual data. Our solution is implemented on an NVIDIA Jetson Xavier running on an actual drone, requiring about 28 milliseconds of processing per RGB/IR image pair.
University of South Florida（南佛罗里达大学）；Center for Urban Transportation Research（城市交通研究中心）；Florida Department of Transportation（佛罗里达州交通部）
There is a rapid growth of applications of Unmanned Aerial Vehicles (UAVs) in traffic management, such as traffic surveillance, monitoring, and incident detection. However, the existing literature lacks solutions to real-time incident detection while addressing privacy issues in practice. This study explored real-time vehicle detection algorithms on both visual and infrared cameras and conducted experiments comparing their performance. Red Green Blue (RGB) videos and thermal images were collected from a UAS platform along highways in the Tampa, Florida, area. Experiments were designed to quant…
Performance Optimization of YOLO-FEDER FusionNet for Robust Drone Detection in Visually Complex Environments
Institute for the Protection of Terrestrial Infrastructures, German Aerospace Center（地面基础设施保护研究所，德国航天中心）；ACIDA Lab, University of Applied Sciences Darmstadt（达姆施塔特应用科学大学ACIDA实验室）；European University of Technology（欧洲技术大学）
RGB-D semantic segmentation methods conventionally use two independent encoders to extract features from the RGB and depth data. However, there lacks an effective fusion mechanism to bridge the encoders, for the purpose of fully exploiting the complementary information from multiple modalities. This paper proposes a novel bottom-up interactive fusion structure to model the interdependencies between the encoders. The structure introduces an interaction stream to interconnect the encoders. The interaction stream not only progressively aggregates modality-specific features from the encoders but a…
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）
Depth information matters in RGB-D semantic segmentation task for providing additional geometric information to color images. Most existing methods exploit a multi-stage fusion strategy to propagate depth feature to the RGB branch. However, at the very deep stage, the propagation in a simple element-wise addition manner can not fully utilize the depth information. We propose Global-Local propagation network (GLPNet) to solve this problem. Specifically, a local context fusion module(L-CFM) is introduced to dynamically align both modalities before element-wise fusion, and a global context fusion…
College of Information Science & Electronic Engineering, Zhejiang University（浙江大学信息科学与电子工程学院）
RGB-D salient object detection aims to identify the most visually distinctive objects in a pair of color and depth images. Based upon an observation that most of the salient objects may stand out at least in one modality, this paper proposes an adaptive fusion scheme to fuse saliency predictions generated from two modalities. Specifically, we design a two-streamed convolutional neural network (CNN), each of which extracts features and predicts a saliency map from either RGB or depth modality. Then, a saliency fusion module learns a switch map that is used to adaptively fuse the predicted salie…
University of Science and Technology of China（中国科学技术大学）；Sichuan University（四川大学）
RGB-D salient object detection (SOD) recently has attracted increasing research interest and many deep learning methods based on encoder-decoder architectures have emerged. However, most existing RGB-D SOD models conduct feature fusion either in the single encoder or the decoder stage, which hardly guarantees sufficient cross-modal fusion ability. In this paper, we make the first attempt in addressing RGB-D SOD through 3D convolutional neural networks. The proposed model, named RD3D, aims at pre-fusion in the encoder stage and in-depth fusion in the decoder stage to effectively promote the ful…
RXFOOD: Plug-in RGB-X Fusion for Object of Interest Detection
Cleveland State University（克利夫兰州立大学）；Centre for Frontier AI Research (CFAR)（前沿人工智能研究中心）；Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局）；Institute of High Performance Computing (IHPC)（高性能计算研究所）；Brookhaven National Laboratory（布鲁克海文国家实验室）
EGM-Det: Entropy-Guided Multimodal Adaptive Fusion for UAV RGB-IR Object Detection
School of Cybersecurity, Northwestern Polytechnical University（西北工业大学网络空间安全学院）；School of Automation and Software Engineering, Shanxi University（山西大学自动化与软件学院）
Residual Spatial Fusion Network for RGB-Thermal Semantic Segmentation
Hangzhou Dianzi University（杭州电子科技大学）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东人工智能与数字经济实验室（深圳））；Zhejiang University（浙江大学）
Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳人工智能与机器人研究院）；School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）理工学院）
Recently, RGB-Thermal based perception has shown significant advances. Thermal information provides useful clues when visual cameras suffer from poor lighting conditions, such as low light and fog. However, how to effectively fuse RGB images and thermal data remains an open challenge. Previous works involve naive fusion strategies such as merging them at the input, concatenating multi-modality features inside models, or applying attention to each data modality. These fusion strategies are straightforward yet insufficient. In this paper, we propose a novel fusion method named Explicit Attention…
Sichuan University（四川大学）；Nankai University（南开大学）；Inception Institute of Artificial Intelligence（起源人工智能研究院）；Wuhan University（武汉大学）
This paper proposes a novel joint learning and densely-cooperative fusion (JL-DCF) architecture for RGB-D salient object detection. Existing models usually treat RGB and depth as independent information and design separate networks for feature extraction from each. Such schemes can easily be constrained by a limited amount of training data or over-reliance on an elaborately-designed training process. In contrast, our JL-DCF learns from both RGB and depth inputs through a Siamese network. To this end, we propose two effective components: joint learning (JL), and densely-cooperative fusion (DCF)…
Scene depth information can help visual information for more accurate semantic segmentation. However, how to effectively integrate multi-modality information into representative features is still an open problem. Most of the existing work uses DCNNs to implicitly fuse multi-modality information. But as the network deepens, some critical distinguishing features may be lost, which reduces the segmentation performance. This work proposes a unified and efficient feature selectionand-fusion network (FSFNet), which contains a symmetric cross-modality residual fusion module used for explicit fusion o…
The reasonable employment of RGB and depth data show great significance in promoting the development of computer vision tasks and robot-environment interaction. However, there are different advantages and disadvantages in the early and late fusion of the two types of data. Besides, due to the diversity of object information, using a single type of data in a specific scenario tends to result in semantic misleading. Based on the above considerations, we propose an adaptively-cooperative fusion network (ACFNet) with ResinRes structure for salient object detection. This structure is designed to fl…
LiDARs and cameras are the two main sensors that are planned to be included in many announced autonomous vehicles prototypes. Each of the two provides a unique form of data from a different perspective to the surrounding environment. In this paper, we explore and attempt to answer the question: is there an added benefit by fusing those two forms of data for the purpose of semantic segmentation within the context of autonomous driving? We also attempt to show at which level does said fusion prove to be the most useful. We evaluated our algorithms on the publicly available SemanticKITTI dataset.…
Most existing lightweight RGB-D salient object detection (SOD) models are based on two-stream structure or single-stream structure. The former one first uses two sub-networks to extract unimodal features from RGB and depth images, respectively, and then fuses them for SOD. While, the latter one directly extracts multi-modal features from the input RGB-D images and then focuses on exploiting cross-level complementary information. However, two-stream structure based models inevitably require more parameters and single-stream structure based ones cannot well exploit the cross-modal complementary …
This paper presents an end-to-end 3D convolutional network named attention-based multi-modal fusion network (AMFNet) for the semantic scene completion (SSC) task of inferring the occupancy and semantic labels of a volumetric 3D scene from single-view RGB-D images. Compared with previous methods which use only the semantic features extracted from RGB-D images, the proposed AMFNet learns to perform effective 3D scene completion and semantic segmentation simultaneously via leveraging the experience of inferring 2D semantic segmentation from RGB-D images as well as the reliable depth cues in spati…
German Research Center for Artificial Intelligence (DFKI GmbH)（德国人工智能研究中心（DFKI 有限公司））；University of Kaiserslautern（凯泽斯劳滕大学）
In this paper, we propose a neural network architecture for scale-invariant semantic segmentation using RGB-D images. We utilize depth information as an additional modality apart from color images only. Especially in an outdoor scene which consists of different scale objects due to the distance of the objects from the camera. The near distance objects consist of significantly more pixels than the far ones. We propose to incorporate depth information to the RGB data for pixel-wise semantic segmentation to address the different scale objects in an outdoor scene. We adapt to a well-known DeepLab-…
Removal then Selection: A Coarse-to-Fine Fusion Perspective for RGB-Infrared Object Detection
Institute of Artificial Intelligence, Hangzhou Innovation Institute, Beihang University, Beijing（北京航空航天大学人工智能研究院，杭州创新院）；Institute of Artificial Intelligence, Beihang University（北京航空航天大学人工智能研究院）；School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；Beijing Institute of Control and Electronic Technology（北京控制与电子技术研究所）
WildLIFT: Lifting monocular drone video to 3D for species-agnostic wildlife monitoring
Fondazione Bruno Kessler（布雷诺·科塞勒基金会）；University of Muenster（明斯特大学）；Max Planck Institute of Animal Behaviour（动物行为马克斯·普朗克研究所）；University of Konstanz（康斯坦茨大学）
Bridging the RGB-IR Gap: Consensus and Discrepancy Modeling for Text-Guided Multispectral Detection
Department of Automation, Tsinghua University（清华大学自动化系）；State Key Laboratory of Blockchain and Data Security, Zhejiang University（浙江大学区块链与数据安全全国重点实验室）；Tsinghua University（清华大学）
Multispectral Detection Transformer with Infrared-Centric Feature Fusion
Artificial Intelligence Graduate School, Gwangju Institute of Science and Technology (GIST)（人工智能研究生院，全州科学技术院（GIST））；School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST)（电气工程与计算机科学学院，全州科学技术院（GIST））
AdaFuse-Det: Adaptive Cross-Modal Fusion of Event Cameras for Robust Object Detection in Low-Light RGB Imagery
SRM University AP, India（印度SRM大学AP分校）；Aptiv, Bengaluru, India（印度Aptiv公司，班加罗尔）；Arizona State University, USA（美国亚利桑那州立大学）；Sejong University, South Korea（韩国世宗大学）；Indian Institute of Information Technology Sri City, India（印度Sri City信息学院）
Tencent Jarvis Lab（腾讯Jarvis实验室）；University of Science and Technology of China（中国科学技术大学）
Automated salient object detection (SOD) plays an increasingly crucial role in many computer vision applications. By reformulating the depth information as supervision rather than as input, depth-supervised convolutional neural networks (CNN) have achieved promising results on both RGB and RGB-D SOD scenarios with the merits of no requirements for extra depth networks and depth inputs in the inference stage. This paper, for the first time, seeks to expand the applicability of depth supervision to the Transformer architecture. Specifically, we develop a Depth-supervised Fusion TRansformer (DFTR…
Interdisciplinary Program in AI, Seoul National University（首尔国立大学人工智能跨学科项目）；Artificial Intelligence Institute, Seoul National University（首尔国立大学人工智能研究所）；Department of Computer Science, Seoul National University（首尔国立大学计算机科学系）
The existing fusion based RGB-D salient object detection methods usually adopt the bi-stream structure to strike the fusion trade-off between RGB and depth (D). The D quality usually varies from scene to scene, while the SOTA bi-stream approaches are depth quality unaware, which easily result in substantial difficulties in achieving complementary fusion status between RGB and D, leading to poor fusion results in facing of low-quality D. Thus, this paper attempts to integrate a novel depth quality aware subnet into the classic bi-stream structure, aiming to assess the depth quality before condu…
Beihang University（北京航空航天大学）；Jiangxi University of Finance and Economics（江西财经大学）；Stony Brook University（石溪大学）；Qingdao University（青岛大学）
Existing RGB-D salient object detection methods treat depth information as an independent component to complement its RGB part, and widely follow the bi-stream parallel network architecture. To selectively fuse the CNNs features extracted from both RGB and depth as a final result, the state-of-the-art (SOTA) bi-stream networks usually consist of two independent subbranches; i.e., one subbranch is used for RGB saliency and the other aims for depth saliency. However, its depth saliency is persistently inferior to the RGB saliency because the RGB component is intrinsically more informative than t…
The 3D scene understanding is mainly considered as a crucial requirement in computer vision and robotics applications. One of the high-level tasks in 3D scene understanding is semantic segmentation of RGB-Depth images. With the availability of RGB-D cameras, it is desired to improve the accuracy of the scene understanding process by exploiting the depth features along with the appearance features. As depth images are independent of illumination, they can improve the quality of semantic labeling alongside RGB images. Consideration of both common and specific features of these two modalities imp…
Nankai University（南开大学）；Tianjin University of Finance and Economics（天津财经大学）；Tsinghua University（清华大学）
Salient object detection (SOD) focuses on distinguishing the most conspicuous objects in the scene. However, most related works are based on RGB images, which lose massive useful information. Accordingly, with the maturity of thermal technology, RGB-T (RGB-Thermal) multi-modality tasks attain more and more attention. Thermal infrared images carry important information which can be used to improve the accuracy of SOD prediction. To accomplish it, the methods to integrate multi-modal information and suppress noises are critical. In this paper, we propose a novel network called Interactive Contex…
This paper presents two variations of architecture referred to as RANet and BIRANet. The proposed architecture aims to use radar signal data along with RGB camera images to form a robust detection network that works efficiently, even in variable lighting and weather conditions such as rain, dust, fog, and others. First, radar information is fused in the feature extractor network. Second, radar points are used to generate guided anchors. Third, a method is proposed to improve region proposal network targets. BIRANet yields 72.3/75.3% average AP/AR on the NuScenes dataset, which is better than t…
Zhejiang University（浙江大学）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Hangzhou SurImage Technology Co., Ltd.（杭州视芯科技有限公司）
Semantic segmentation has made striking progress due to the success of deep convolutional neural networks. Considering the demands of autonomous driving, real-time semantic segmentation has become a research hotspot these years. However, few real-time RGB-D fusion semantic segmentation studies are carried out despite readily accessible depth information nowadays. In this paper, we propose a real-time fusion semantic segmentation network termed RFNet that effectively exploits complementary cross-modal information. Building on an efficient network architecture, RFNet is capable of running swiftl…
COXNet: Cross-Layer Fusion with Adaptive Alignment and Scale Integration for RGBT Tiny Object Detection
Beijing Institute of Technology（北京理工大学）；National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台）；National Key Laboratory of Space Target Awareness, Space Engineering University（航天工程大学空间目标感知全国重点实验室）；Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education of China（教育部光电成像技术与系统重点实验室）；Chongqing Innovation Center, Beijing Institute of Technology（北京理工大学重庆创新中心）；China North Vehicle Research Institute（中国北方车辆研究所）
