Pedestrian Detection on TJU-Ped-traffic

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R (miss rate)

Rank	Model	R (miss rate)	RS (miss rate)	HO (miss rate)	R+HO (miss rate)	ALL (miss rate)	Paper	Code	Year
1	LSFM	18.7	24.9	56.2	–	–	Localized Semantic Feature Mixers for Efficient Pedestrian Detection in Autonomous Driving		2023
2	EGCL	19.73	-	60.05	24.19	35.76	Pedestrian Detection by Exemplar-Guided Contrastive Learning		2021
3	CrowdDet	20.82	-	61.22	25.28	36.94	Detection in Crowded Scenes: One Proposal, Multiple Predictions	tusimple/simpledet · Purkialo/CrowdDet · megvii-model/CrowdDetection	2020
4	FPN	22.30	35.19	60.30	26.71	37.78	Feature Pyramid Networks for Object Detection	PaddlePaddle/PaddleOCR · open-mmlab/mmdetection · facebookresearch/detectron · +82	2016
5	RetinaNet	23.89	37.92	61.60	28.45	41.40	Focal Loss for Dense Object Detection	tensorflow/models · facebookresearch/detectron2 · open-mmlab/mmdetection · +231	2017
6	FCOS	24.35	37.40	63.73	28.86	40.02	FCOS: Fully Convolutional One-Stage Object Detection	open-mmlab/mmdetection · pytorch/vision · PaddlePaddle/PaddleDetection · +84	2019
7	LSFM	18.7	24.9	56.2	–	–	Localized Semantic Feature Mixers for Efficient Pedestrian Detection in Autonomous Driving		2023
8	EGCL	19.73	-	60.05	24.19	35.76	Pedestrian Detection by Exemplar-Guided Contrastive Learning		2021
9	CrowdDet	20.82	-	61.22	25.28	36.94	Detection in Crowded Scenes: One Proposal, Multiple Predictions	tusimple/simpledet · Purkialo/CrowdDet · megvii-model/CrowdDetection	2020
10	FPN	22.30	35.19	60.30	26.71	37.78	Feature Pyramid Networks for Object Detection	PaddlePaddle/PaddleOCR · open-mmlab/mmdetection · facebookresearch/detectron · +82	2016
11	RetinaNet	23.89	37.92	61.60	28.45	41.40	Focal Loss for Dense Object Detection	tensorflow/models · facebookresearch/detectron2 · open-mmlab/mmdetection · +231	2017
12	FCOS	24.35	37.40	63.73	28.86	40.02	FCOS: Fully Convolutional One-Stage Object Detection	open-mmlab/mmdetection · pytorch/vision · PaddlePaddle/PaddleDetection · +84	2019