目标检测(OBJ)

Num	Title	Field	Desc	Author	Time	read
	Rich feature hierarchies for accurate object detection and semantic segmentation	目标检测	R-CNN
	Fast R-CNN	目标检测	Fast R-CNN
	Faster R-CNN：Towards Real-Time Object	目标检测	Faster R-CNN
	Mask R-CNN	目标检测	Mask R-CNN
	SSD:Single Shot MultiBox Detector	目标检测	SSD
	Mobilenet-SSDv2: An Improved Object Detection Model for Embedded Systems	目标检测	Mobilenet-SSDv2
	Feature Pyramid Networks for Object Detection	目标检测	FPN
	Fully Convolutional Networks for Semantic Segmentation	图像分割	FCN
	FCOS: Fully Convolutional One-Stage Object Detection	目标检测	FCOS
	Focal Loss for Dense Object Detection	目标检测	RetinaNet
	Bag of Freebies for Training Object Detection Neural Networks	目标检测
	You Only Look One-Unified, Real-Time Object Detection	目标检测	YOLOv1
	YOLO9000：Better, Faster, Stronger	目标检测	YOLOv2
	YOLOv3：An Incremental Improvement	目标检测	YOLOv3
	YOLOv4：Optimal Speed and Accuracy of Object Detection	目标检测	YOLOv4
	PP-YOLO：An Effective and Efficient Implementation of Object Detector	目标检测	PP-YOLO
	PP-YOLOv2：A Practical Object Detector	目标检测	PP-YOLO2
	YOLOv4: Optimal Speed and Accuracy of Object Detection	目标检测	YOLOv4
	YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications	目标检测	YOLOv6
	YOLOv6 v3.0: A Full-Scale Reloading	目标检测	YOLOv6 v3.0
	YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors	目标检测	YOLOv7
	YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information	目标检测	YOLOv9
	YOLOv10: Real-Time End-to-End Object Detection	目标检测	YOLOv10
	YOLOX: Exceeding YOLO Series in 2021	目标检测	YOLOX
	YOLOF: You Only Look One-level Feature	目标检测	YOLOF
	YOLOP: You Only Look Once for Panoptic Driving Perception	目标检测	YOLOP
	YOLOR: BASED MULTI-TASK LEARNING	目标检测	YOLOR
	YOLOS: You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection	目标检测	YOLOS
	YOLOOC: YOLO-based Open-Class Incremental Object Detection with Novel Class Discovery	目标检测	YOLOOC
	Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection	目标检测	ATSS
	Learning Spatial Fusion for Single-Shot Object Detection	目标检测	ASFF
	Cascade R-CNN: Delving into High Quality Object Detection	目标检测	Cascade-RCNN
	CenterMask: Real-Time Anchor-Free Instance Segmentation	目标检测	CenterMask
	DAMO-YOLO : A Report on Real-Time Object Detection Design	目标检测	DAMO-YOLO
	End-to-End Object Detection with Transformers	目标检测	DETR
	DynamicDet: A Unified Dynamic Architecture for Object Detection	目标检测	DY-yolov7
	Edge YOLO: Real-Time Intelligent Object Detection System Based on Edge-Cloud Cooperation in Autonomous Vehicles	目标检测	Edge-YOLO
	EfficientDet: Scalable and Efficient Object Detection	目标检测	EfficientDet
	FemtoDet: An Object Detection Baseline for Energy Versus Performance Tradeoffs	目标检测	FemtoDet
	Gold-YOLO: Efficient Object Detector via Gather-and-Distribute Mechanism	目标检测	Gold-YOLO
	MDETR - Modulated Detection for End-to-End Multi-Modal Understanding	目标检测	MDETR
	Mobilenet-SSDv2: An Improved Object Detection Model for Embedded Systems	目标检测	Mobilenet-SSDv2
			MS-DAYOLO
	OneNet: Towards End-to-End One-Stage Object Detection		OneNet
	Simple Open-Vocabulary Object Detection with Vision Transformers		OWL-ViT
	Scaling Open-Vocabulary Object Detection		OWLv2
	PP-YOLO: An Effective and Efficient Implementation of Object Detector		PP-YOLOv1
	PP-YOLOv2: A Practical Object Detector		PP-YOLOv2
	R-FCN: Object Detection via Region-based Fully Convolutional Networks		R-FCN
	Rich feature hierarchies for accurate object detection and semantic segmentation		RCNN
	RDSNet: A New Deep Architecture for Reciprocal Object Detection and Instance Segmentation		RDSNet
	Focal Loss for Dense Object Detection		RetinaNet
	Focal Loss for Dense Object Detection		RT-DETR
	RTMDet: An Empirical Study of Designing Real-Time Object Detectors		RTMDet
	Side-Aware Boundary Localization for More Precise Object Detection		SABL
	Scaled-YOLOv4: Scaling Cross Stage Partial Network		Scaled-YOLOv4
	Simple Multi-dataset Detection		Simple Multi-dataset Detection
	SM-NAS: Structural-to-Modular Neural Architecture Search for Object Detection		SM-NAS
	Sparse R-CNN: End-to-End Object Detection with Learnable Proposals		Sparse R-CNN
	Sparse Instance Activation for Real-Time Instance Segmentation		SparseInst
	VarifocalNet: An IoU-aware Dense Object Detector		VarifocalNet
	ViT-YOLO: Transformer-Based YOLO for Object Detection		ViT-YOLO
	YOLO-MS: Rethinking Multi-Scale Representation Learning for Real-time Object Detection		YOLO-MS
	YOLO-World: Real-Time Open-Vocabulary Object Detection		YOLO-World
	High-Performance Fine Defect Detection in Artificial Leather Using Dual Feature Pool Object Detection		YOLOD
	You Only Look One-level Feature		YOLOF
	YOLOOC		YOLOOC
	YOLOP: You Only Look Once for Panoptic Driving Perception		YOLOP
	YOLOR-BASED MULTI-TASK LEARNING		YOLOR
	You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection		YOLOS