Object detection in natural scenes can be a challenging task. In many real-life situations, the visible spectrum is not suitable for traditional computer vision tasks. Moving outside the visible spectrum range, such as the thermal spectrum or the near-infrared (NIR) images, is much more beneficial in low visibility conditions, NIR images are very helpful for understanding the object's material quality. In this work, we have taken images with both the Thermal and NIR spectrum for the object detection task. As multi-spectral data with both Thermal and NIR is not available for the detection task, we needed to collect data ourselves. Data collection is a time-consuming process, and we faced many obstacles that we had to overcome. We train the YOLO v3 network from scratch to detect an object from multi-spectral images. Also, to avoid overfitting, we have done data augmentation and tune hyperparameters.
From Words to Wavelengths: VLMs for Few-Shot Multispectral Object Detection
Univ Bretagne Sud, IRISA, UMR 6074（布列塔尼大学，IRISA，UMR 6074）；Univ Rennes, IRISA, UMR 6074（里尔大学，IRISA，UMR 6074）；ATERMES；UiT The Arctic University of Norway（北欧大学）
Optimizing Multispectral Object Detection: A Bag of Tricks and Comprehensive Benchmarks
Beijing Forestry University（北京林业大学）；Hong Kong University of Science and Technology（香港科技大学）；State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS)（多模态人工智能系统国家重点实验室）；National University of Singapore（新加坡国立大学）；Nanyang Technological University（南洋理工大学）；Sun Yat-sen University（中山大学）
YOLOv11-RGBT: Towards a Comprehensive Single-Stage Multispectral Object Detection Framework
School of Instrument Science and Opto-electronics Engineering, Anhui Province Key Laboratory of Measuring Theory and Precision Instrument（仪器科学与光电工程学院、安徽省测量理论与精密仪器重点实验室）；Hefei University of Technology（合肥工业大学）；School of Information Engineering（信息工程学院）；Engineering University of PAP（工程大学）
DMM: Disparity-guided Multispectral Mamba for Oriented Object Detection in Remote Sensing
University of Electronic Science and Technology of China（电子科技大学）；Beijing Institute of Space Mechanics and Electricity（北京空间机电研究所）；Donghai Laboratory（东海实验室）
Multispectral State-Space Feature Fusion: Bridging Shared and Cross-Parametric Interactions for Object Detection
School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, 212013, China（江苏大学电气与信息工程学院）；Department of Computer Science and Engineering, University of North Texas, Denton, TX 76207, USA（德克萨斯大学北卡罗来纳分校计算机科学与工程系）；School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, China（江苏科技大学计算机科学与工程学院）；School of Automation, Southeast University, Nanjing, 210096, China（东南大学自动化学院）；Bodhi Intelligence Lab, Department of Artificial Intelligence, Westlake University, Hangzhou, Zhejiang 310030, China（西湖大学人工智能研究院）
DAMSDet: Dynamic Adaptive Multispectral Detection Transformer with Competitive Query Selection and Adaptive Feature Fusion
School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications（重庆邮电大学通信与信息工程学院）；School of Mathematics and Statistics, Xi’an Jiaotong University（西安交通大学数学与统计学院）；School of Intelligent Systems Engineering, Sun Yat-sen University（中山大学智能系统工程学院）
Dual Sparse Aggregation Transformer for Multispectral Object Detection
Northwestern Polytechnical University（西北工业大学）；School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）；School of Cybersecurity, Northwestern Polytechnical University（西北工业大学网络空间安全学院）；Shaanxi Provincial Key Laboratory of Speech and Image Information Processing（陕西省语音与图像信息处理重点实验室）；National Engineering Laboratory for Integrated Aerospace-Ground-Ocean Big Data Application Technology（空天地海一体化大数据应用技术国家工程实验室）
Rethinking Early-Fusion Strategies for Improved Multispectral Object Detection
College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息科学与电子工程学院）；School of Information and Electrical Engineering, Hangzhou City University（杭州城市大学信息与电气工程学院）；Hangzhou City University Binjiang Innovation Center（杭州城市大学滨江创新中心）；Jinhua Institute of Zhejiang University（浙江大学金华研究院）
SFFR: Spatial-Frequency Feature Reconstruction for Multispectral Aerial Object Detection
School of Computer, Jiangsu University of Science and Technology（江苏科技大学计算机学院）；School of Electrical and Information Engineering, Jiangsu University（江苏大学电气与信息工程学院）；School of Automation, Southeast University（东南大学自动化学院）
Semantic segmentation of multispectral photoacoustic images using deep learning
German Cancer Research Center (DKFZ)（德国癌症研究中心（DKFZ））；Heidelberg University（海德堡大学）；HIDSS4Health - Helmholtz Information and Data Science School for Health（HIDSS4Health——亥姆霍兹健康信息与数据科学学院）；Cancer Research UK Cambridge Institute（英国癌症研究院剑桥研究所）；University of Cambridge（剑桥大学）
3D LULC classification using multispectral LiDAR and deep learning: current and prospective schemes
D Optical Metrology (3DOM) Unit, Bruno Kessler Foundation (FBK)（3D光学计量（3DOM）单元，布鲁诺·凯塞尔基金会（FBK））；Department of Geodesy and Geoinformation, TU Wien（测绘与地理信息系，维也纳技术大学）；Helmholtz-ZentrumDresden-Rossendorf (HZDR), Helmholtz Institute Freiberg for Resource Technology (HIF)（德累斯顿-罗斯托克赫尔姆霍尔茨研究中心（HZDR），资源技术赫尔姆霍尔茨研究所（HIF））；Freie Universität Berlin, Remote Sensing and Geoinformatics（柏林自由大学，遥感与地理信息学）；Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute FGI, The National Land Survey of Finland（芬兰地理研究所（FGI），芬兰国家土地测绘局）
DEPFusion: Dual-Domain Enhancement and Priority-Guided Mamba Fusion for UAV Multispectral Object Detection
School of Information Engineering, Guangdong University of Technology（广东工业大学信息工程学院）；School of Electronic and Information Engineering, South China University of Technology（华南理工大学电子信息学院）；School of Artificial Intelligence, XiDian University（西安电子科技大学人工智能学院）
DLRMamba: Distilling Low-Rank Mamba for Edge Multispectral Fusion Object Detection
National Space Science Center, Chinese Academy of Sciences（中国科学院国家空间科学中心）；School of Computer Science and Technology, University of Chinese Academy of Sciences（中国科学院大学计算机科学与技术系）；School of Electronic Engineering and Computer Science, Queen Mary University of London（伦敦大学Queen Mary电子工程与计算机科学学院）；School of Astronomy and Space Science, University of Chinese Academy of Sciences（中国科学院大学天文与空间科学系）
Multispectral Vineyard Segmentation: A Deep Learning approach
University of Coimbra（科英布拉大学）；Institute for Systems Engineering and Computers at Coimbra（科英布拉系统工程与计算机研究所）；Polytechnic Institute of Coimbra（科英布拉理工学院）；Stockholm University（斯德哥尔摩大学）；Bolin Centre for Climate Research（博林气候研究中心）；Navarino Environmental Observatory（纳瓦里诺环境观测站）
Progressive Pixel-Neighborhood Deformable Cross-Attention for Multispectral Object Detection
School of Electrical and Information Engineering, Jiangsu University（江苏大学电气与信息工程学院）；School of Computer Science and Engineering, Jiangsu University of Science and Technology（江苏科技大学计算机科学与工程学院）
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）
MO R-CNN: Multispectral Oriented R-CNN for Object Detection in Remote Sensing Image
College of Electronics and Information Engineering, Sichuan University（四川大学电子信息工程学院）；School of Mechanical Engineering and Automation, Fuzhou University（福州大学机械工程与自动化学院）；Yunnan Key Laboratory of Software Engineering, Yunnan University（云南软件工程重点实验室）
针对多光谱定向目标检测中模态差异与计算开销问题，提出轻量级框架 MO R-CNN，通过异构特征提取、单模态监督和条件标签融合提升检测性能。
School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, 212013, China（江苏大学电气与信息工程学院）；School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, China（江苏科技大学计算机科学与工程学院）；School of Automation, Southeast University, Nanjing, 210096, China（东南大学自动化学院）；Department of Computer Science and Engineering, University of North Texas, Denton, TX 76207, USA（北卡罗来纳州立大学计算机科学与工程系）；School of Computing, Nanjing Normal University, Nanjing, 210046, Jiangsu（南京师范大学计算机学院）
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））
Evaluation of Deep Learning Semantic Segmentation for Land Cover Mapping on Multispectral, Hyperspectral and High Spatial Aerial Imagery
Center for Space and Remote Sensing Research, National Central University（国立中央大学太空及遥测研究中心）；Department of Computer Science and Information Engineering, National Central University（国立中央大学资讯工程学系）
3D forest semantic segmentation using multispectral LiDAR and 3D deep learning
Bruno Kessler Foundation (FBK)（布鲁诺·凯塞勒基金会）；TU Wien（维也纳工业大学）；Finnish Geospatial Research Institute（芬兰地理空间研究所）；National Land Survey of Finland（芬兰国家土地测量局）
Unsupervised deep learning for semantic segmentation of multispectral LiDAR forest point clouds
Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute FGI, The National Land Survey of Finland（遥感与摄影测量系，芬兰地理研究 institute FGI，芬兰国家土地测绘局）；D Optical Metrology (3DOM) unit, Bruno Kessler Foundation (FBK)（3D光学计量（3DOM）单元，布鲁诺·凯瑟尔基金会（FBK））；Department of Built Environment, Aalto University, School of Engineering（环境学院，艾尔托大学，工程学院）
Unsupervised semantic segmentation of urban high-density multispectral point clouds
Finnish Geospatial Research Institute FGI, the National Land Survey of Finland（芬兰地理空间研究所（FGI，芬兰国家土地测量局））；Department of Built Environment（建筑环境系）；School of Engineering（工程学院）；Aalto University（阿尔托大学）
本文提出无监督地面感知深度聚类方法 GroupSP，通过超点分组与伪标签迭代训练，实现高密度多光谱 ALS 城市点云的七类语义分割，并以极少标注取得高精度。
TFDet: Target-Aware Fusion for RGB-T Pedestrian Detection
College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息与电子工程学院）；Jinhua Institute of Zhejiang University（浙江大学金华研究院）；School of Computer Science, University of Nottingham Ningbo China（宁波诺丁汉大学计算机学院）
Riverine Land Cover Mapping through Semantic Segmentation of Multispectral Point Clouds
Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute FGI（遥感与摄影测量系，芬兰地理研究所）；Faculty of Technology, University of Turku（技术学院，图尔库大学）；Department of Geography and Geology, University of Turku（地理与地质系，图尔库大学）；Faculty of Geological Sciences, Complutense University of Madrid（地质科学学院，马德里卡洛斯三世大学）
First Lessons Learned of an Artificial Intelligence Robotic System for Autonomous Coarse Waste Recycling Using Multispectral Imaging-Based Methods
Hamburg University of Applied Sciences（汉堡应用技术大学）；University of the West of Scotland（西苏格兰大学）；Institute for Energy, Recycling and Environmental Protection at Bremen University of Applied Sciences（不来梅应用技术大学能源、回收与环境保护研究所）；German Research Center for Artificial Intelligence（德国人工智能研究中心）
SIGMAE: A Spectral-Index-Guided Foundation Model for Multispectral Remote Sensing
School of Artificial Intelligence, Wuhan University（武汉大学人工智能学院）；School of Artificial Intelligence and Automation, Wuhan University of Science and Technology（武汉科技大学人工智能与自动化学院）；School of Electronic Information, Wuhan University of Science and Technology（武汉科技大学电子信息学院）；School of Computer Science, Wuhan University（武汉大学计算机学院）
DPDETR: Decoupled Position Detection Transformer for Infrared-Visible Object Detection
School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications（通信与信息工程学院，重庆邮电大学）；School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University（智能系统工程学院，中山大学深圳校区）；School of Mathematics and Statistics, Xi’an Jiaotong University（数学与统计学院，西安交通大学）
