Dense Object Detection

20 papers with code • 1 benchmarks • 3 datasets

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Libraries

Use these libraries to find Dense Object Detection models and implementations

Most implemented papers

Focal Loss for Dense Object Detection

facebookresearch/detectron ICCV 2017

Our novel Focal Loss focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the detector during training.

PP-YOLOE: An evolved version of YOLO

PaddlePaddle/PaddleDetection 30 Mar 2022

In this report, we present PP-YOLOE, an industrial state-of-the-art object detector with high performance and friendly deployment.

Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection

implus/GFocal NeurIPS 2020

Specifically, we merge the quality estimation into the class prediction vector to form a joint representation of localization quality and classification, and use a vector to represent arbitrary distribution of box locations.

Precise Detection in Densely Packed Scenes

eg4000/SKU110K_CVPR19 CVPR 2019

We propose a novel, deep-learning based method for precise object detection, designed for such challenging settings.

Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection

implus/GFocalV2 CVPR 2021

Such a property makes the distribution statistics of a bounding box highly correlated to its real localization quality.

Soft Anchor-Point Object Detection

xuannianz/FSAF ECCV 2020

In this work, we boost the performance of the anchor-point detector over the key-point counterparts while maintaining the speed advantage.

AutoAssign: Differentiable Label Assignment for Dense Object Detection

Megvii-BaseDetection/AutoAssign 7 Jul 2020

During training, to both satisfy the prior distribution of data and adapt to category characteristics, we present Center Weighting to adjust the category-specific prior distributions.

BorderDet: Border Feature for Dense Object Detection

Megvii-BaseDetection/BorderDet ECCV 2020

In this paper, We propose a simple and efficient operator called Border-Align to extract "border features" from the extreme point of the border to enhance the point feature.

A Solution to Product detection in Densely Packed Scenes

Media-Smart/SKU110K-DenseDet 23 Jul 2020

To grasp the essential feature of the densely packed scenes, we analysis the stages of a detector and investigate the bottleneck which limits the performance.

Localization Distillation for Dense Object Detection

HikariTJU/LD CVPR 2022

Previous KD methods for object detection mostly focus on imitating deep features within the imitation regions instead of mimicking classification logit due to its inefficiency in distilling localization information and trivial improvement.