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Deep learning object detection

2022-04-23 05:30:00 【Mikawa】

deep learning object detection

Paper list from 2014 to 2019

deep_learning_object_detection_history

Milestones

milestone

milestone

Object detector composed parts

  • Input: Image, Patches, Image Pyramid
  • Backbones: VGG16, ResNet-50, SpineNet, EfficientNet-B0/B7 , CSPResNeXt50, CSPDarknet53
  • Neck:
    • Additional blocks: SPP, ASPP, RFB, SAM
    • Path-aggregation blocks: FPN, PAN, NAS-FPN, Fully-connected FPN, BiFPN, ASFF, SFAM
  • Heads:
    • Dense Prediction (one-stage):
      • RPN, SSD, YOLO, RetinaNet (anchor based)
      • CornerNet, CenterNet, MatrixNet, FCOS (anchor free)
    • Sparse Prediction (two-stage):
      • Faster R-CNN, R-FCN, Mask R-CNN (anchor based)
      • RepPoints (anchor free)

Detection methods category

Object detection steps

One-Stage

  1. Extracts feature on all area of image, classify the objects,

    localize bounding-box

Two-Stage

  1. Generates category-independent region proposals,

    extracts feature vector from each region proposal

  2. Classify the objects, precisely bounding-box prediction (NMS)

Small object detection tricks

  • Framework for small object detection

    • Multi-scale Feature Learning

  • Enhance the Receptive Fields (visual attention mechanisms)

  • Data Augmentation

    • GAN-based Detection
    • Flipping, cropping, rotating, scaling

  • Training Strategy

    • Unsupervised object detection
    • Weakly Supervised Object Detection
    • Multi-Scale Training/Val/Test
    • GPU accelerate

  • Context-based Detection

    • Local context
    • Global context
    • Context interactive

  • Neural Architecture Search

    • Stacking more pyramid networks
    • Adding feature dimension
    • Adopting high capacity architecture

  • Efficient post-processing methods

    • Non maximum suppression (NMS)
    • Soft-NMS

  • Deformable convolutional networks

  • Multi-task joint learning and optimization

    • Object detection
    • Semantic segmentation
    • Instance segmentation
    • Edge detection
    • Highlight detection

  • Establish small object datasets

Performance table

FPS(Speed) index is related to the hardware spec(e.g. CPU, GPU, RAM, etc), so it is hard to make an equal comparison. The solution is to measure the performance of all models on hardware with equivalent specifications, but it is very difficult and time consuming.

Detector COCO (mAP@IoU=0.5:0.95) Published In
R-CNN - CVPR’14
Fast R-CNN 19.7 ICCV’15
Faster R-CNN 21.9 NIPS’15
YOLO v1 - CVPR’16
SSD 31.2 ECCV’16
R-FCN 29.9 NIPS’16
FPN 36.2 CVPR’17
YOLO v2 - CVPR’17
RetinaNet 39.1 ICCV’17
Mask R-CNN 39.8 ICCV’17
Soft-NMS 40.9 ICCV’17
YOLO v3 33.0 arXiv’18
RefineDet 41.8 CVPR’18
Cascade R-CNN 42.8 CVPR’ 18
RFBNet - ECCV’18
Softer-NMS - arXiv’ 18
SNIPER 43.5 NIPS’ 18
M2Det 44.2 AAAI’19
Libra R-CNN 43.0 CVPR’19
FSAF 44.6 CVPR’19
ExtremeNet 43.7 CVPR’19
CenterNet 45.1 ICCV’19
FreeAnchor 44.8 NeurIPS’19
CBNet 53.3 AAAI’20
YOLOv4 - arXiv’20
ATSS 50.7 CVPR’ 20
Hit-Detector 41.4 CVPR’ 20
DetectoRS 54.7 arXiv’20

Performance on MS COCO

MS COCO detection evaluation metrics

2014

  • [R-CNN] Rich feature hierarchies for accurate object detection and semantic segmentation | [CVPR’ 14] |[pdf] [official code - caffe] CNN

2015

2016

2017

2018

2019

  • [M2Det] M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network | [AAAI’ 19] |[pdf] [official code - pytorch] Multi-scale Feature Learning

  • [Libra R-CNN] Libra R-CNN: Balanced Learning for Object Detection | [CVPR’ 19] |[pdf] Training Strategy

  • [FSAF] Feature Selective Anchor-Free Module for Single-Shot Object Detection | [CVPR’ 19] |[pdf] Anchor-Free

  • [ExtremeNet] Bottom-up Object Detection by Grouping Extreme and Center Points | [CVPR’ 19] |[pdf] | [official code - pytorch] Instance Segmentation

  • [CenterNet] CenterNet: Keypoint Triplets for Object Detection | [ICCV’ 19] |[pdf] Keypoint-based detector

  • [FreeAnchor] FreeAnchor: Learning to Match Anchors for Visual Object Detection | [NeurIPS’ 19] |[pdf] Anchor-Free

2020

  • [CBnet] Cbnet: A novel composite backbone network architecture for object detection | [AAAI’ 20] |[pdf] Composite Backbone Network
  • [YOLOv4] YOLOv4: Optimal Speed and Accuracy of Object Detection | [arXiv’ 20] |[pdf]
    • Input: Mosaic data augmentation, Cross mini-Batch Normalization (CmBN), Self-adversarial-training (SAT)
    • BackBone: CSPDarknet53, Mish-activation, DropBlock regularization
    • Neck: SPP block, PAN (path-aggregation block)
    • Prediction: CIoU-loss, DIoU-NMS
  • [ATSS] Bridging the Gap Between Anchor-Based and Anchor-Free Detection via Adaptive Training Sample Selection | [CVPR’ 20] |[pdf] Anchor-Based Training Strategy
  • [Hit-Detector] Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection | [CVPR’ 20] |[pdf] Neural Architecture Search
  • [DetectoRS] DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution | [arXiv’ 20] |[pdf] Recursive Feature Pyramid Switchable Atrous Convolution Instance Segmentation

Survey

  • Recent advances in small object detection based on deep learning: A review [pdf]
  • A Survey of Deep Learning-based Object Detection [pdf]
  • Object Detection in 20 Y ears: A Survey [pdf]
  • Recent Advances in Deep Learning for Object Detection [pdf]

Analyze Tools

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https://yzsam.com/2022/04/202204220543094190.html

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