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  *[LiDAR]: Light Detection And Ranging
  *[FLOPS]: Floating-Point Operations per Second
  *[CNN]: Convolutional Neural Network
  *[NIR]: Near-Infrared
  *[MIR]: Middle-Infrared
  *[FIR]: Far-Infrared
  *[OSU]: Ohio State University
  *[LITIV]: Laboratoire d’Interprétation et de Traitement d’Images et Vidéo
  *[LSI-FIR]: Laboratorio de Sistemas Inteligentes Far-Infrared
  *[TIV]: Thermal Infrared Video
  *[KAIST]: Korea Advanced Institute of Science and Technology
  *[NTPD]: Night-Time Pedestrian Dataset
  *[KMU]: Keimyung University
  *[SCUT]: South China University of Technology
  *[FLIR]: Forward Looking InfraRed
  *[ZUT]: Zachodniopomorski Uniwersytet Technologiczny
  *[LLVIP]: Low Light Visible Image Person
  *[ADAS]: Advanced Driver Assistance Systems
  *[CAN bus]: Controller Area Network
  *[IR]: Infrared
  *[FoV]: Field-of-View
  *[ROI]: Region of Interest
  *[ROIs]: Region of Interest
  *[CSM]: Contour Saliency Map
  *[TIR-ACF]: Thermal Infrared Radiometric Cumulative Channel Feature
  *[CaRF]: Cascade Random Forest
  *[OCS-LBPs]: Oriented Center-Symmetric Local Binary Patterns
  *[HOG]: Histogram of Oriented Gradients
  *[HLID]: Histogram of Local Intensity Difference
  *[SVM]: Support Vector Machine
  *[DNNs]: Deep Neural Network
  *[YOLO]: You Only Look Once
  *[CIEM]: Cascaded Information Enhancement Module
  *[CAFFM]: Cross-modal Attention Feature Fusion Module
  *[CAM]: Channel-wise Attention Module
  *[SAM]: Spatial-wise Attention Module
  *[CFCM]: Cross-modality Feature Complementary Module
  *[AFEFM]: Attention-based Feature Enhancement Fusion Module
  *[fps]: frames per second
  *[MR]: Miss Rate
  *[MLF-FRCNN]: Multi-Layer Fusion network based on Faster R-CNN
  *[FPN]: Feature Pyramid Network
  *[RPN]: Region Proposal Network
  *[AP]: Average Precision
  *[CSSA]: Channel Switching and Spatial Attention
  *[GAFF]: Guided Attentive Feature Fusion
  *[CFT]: Cross-modality Fusion Transformer
  *[TIR]: Thermal-Infrared
  *[CNNs]: Convolutional Neural Network
  *[VIS]: Visual-Optical
  *[NIN]: Network-In-Network
  *[R-FCN]: Region-based Fully Convolutional Network
  *[HFA]: High-Frequency Assistant
  *[DCT]: Discrete Cosine Transform
  *[RCAB]: Residual Channel Attention Block
  *[SPA]: Scale‐aware Permutated Attention
  *[AFA]: Adjacent Feature Aggregation
  *[CMDAF]: Cross-Modality Differential Aware Fusion
  *[HAFNet]: Hierarchical Attentive Fusion Network
  *[HCAF]: Hierarchical Content-dependent Attentive Fusion
  *[MFA]: Multi-modality Feature Alignment
  *[Faster R-CNN]: Faster Region-based CNN
  *[CIAN]: Cross-Modality Interactive Attention Network
  *[DMFFNet]: Dual-modal Multi-scale Feature Fusion Network
  *[DDFF]: Double Deep Feature Fusion
  *[MCFF]: Multi-spectral Channel Feature Fusion
  *[GFU]: Gated Fusion Units
  *[GFD-SSD]: Gated Fusion Double SSD
  *[SSD]: Single Shot MultiBox Detector
  *[GFUs]: Gated Fusion Units
  *[RISNet]: Redundant Information Suppression Network
  *[MS-DETR]: MultiSpectral Pedestrian DEtection TRansformer
  *[ResNet-152]: Residual Network-152
  *[mAP]: mean Average Precision
  *[MSFFN]: MultiSpectral Feature Fusion Network
  *[MFEV]: Multiscale Feature Extraction of Visible images
  *[MFEI]: Multiscale Feature Extraction of Infrared images
  *[SKNet]: Selective Kernel Network
  *[CWF]: Channel Weighting Fusion
  *[APF]: Accumulated Probability Fusion
  *[ProbEn]: Probabilistic Ensembling
  *[IT-MN]: Illumination and Temperature-aware Multispectral Network
  *[FWN]: Fusion Weight Network
  *[DSMN]: Double-Stream Multispectral Network
  *[i-IAN]: improved Illumination–Aware Network
  *[IAF R-CNN]: Illumination-Aware Faster R-CNN
  *[ASG-LPF]: Adaptive Soft-Gated Light Perception Fusion
  *[CSPNet]: Center and Scale Prediction Network
  *[UDA]: Unsupervised Domain Adaptation
  *[SSTN]: Self-Supervised Thermal Network
  *[TS-RPN]: Two Stream Region Proposal Network
  *[KD]: Knowledge Distillation
  *[CFL]: Cross-modal Feature Learning
  *[CMKD]: Cross Modality Knowledge Distillation
  *[MD]: Modality Distillation
  *[F-MSE]: Focal Mean Square Error
  *[MANN]: Memory Augmented Neural Network
  *[MSR]: Multi-Spectral Recalling
  *[LPR]: Large-scale Pedestrian Recalling
  *[TE-GAN]: Thermal-Enhancement GAN
  *[WDSR]: Wide Activation Deep Super-Resolution
  *[GAN]: Generative Adversarial Network
  *[ViT]: Vision Transformer
  *[TE-VGAN]: Thermal Enhancement Vision Generative Adversarial Network
  *[SIDNet]: Scene-Illumination Disentangled Network
  *[TCEFNet]: Texture-Contrast Enhancement Fusion Network
  *[FAM]: Feature Attention Module
  *[FTM]: Feature Transformation Module
  *[AED-CNN]: Attention-guided Encoder-Decoder Convolutional Neural Network
  *[AHE]: Adaptive Histogram Equalization
  *[CBN]: Convolutional Backbone Network
  *[SK]: Select Kernel
  *[I2I]: Image-to-Image
  *[GMA-CycleGAN]: Gray Mask Attention-CycleGAN
  *[GV]: Grayscale Visible
  *[CV]: Color Visible
  *[LS-GAN]: Least-Squares GAN
  *[PiCANet]: Pixel-wise Contextual Attention Network
  *[RRRNet]: Recurrent Residual Refinement Network
  *[VST]: Visual Salient Transformer
  *[MB-Net]: Modality Balance Network
  *[DMAF]: Differential Modality Aware Fusion
  *[IAFA]: Illumination Aware Feature Alignment
  *[MuFEm]: Multi-modal Feature Embedding
  *[GAT]: Graph-Attention Network
  *[SCoFA]: Spatio-Contextual Feature Aggregation
  *[RNNs]: Recurrent Neural Network
  *[AR-CNN]: Aligned Region CNN
  *[RFA]: Region Feature Alignment
  *[UFF]: Uncertainty-aware Feature Fusion
  *[UCG]: Uncertainty-aware Cross-modal Guiding
  *[IoU]: Intersection over Union
  *[ARPN]: Automatic Region Proposal Network
  *[GRPN]: Ground-Region Proposal Network
  *[FP]: False Positive
  *[SWIR]: Short-wave Infrared
  *[LWIR]: Long-wave Infrared
  *[AWGN]: Additive Random White Gaussian Noise
  *[HE]: Histogram Equalization
  *[BDT]: Boosted Decision Tree
  *[RRN]: Region Reconstruction Network
  *[MSDN]: Multi-Scale Detection Network
  *[SVR]: Support Vector Regression
  *[TV]: Total Variation