【Paper】 Deep Learning for Generic Object Detection, A Survey - Summary

14 Jan 2021 in Artificial Intelligence

Deep Learning for Generic Object Detection, A Survey 논문 리뷰 및 정리

Deep Learning for Generic Object Detection: A Survey

My Conclusion after I read the paper.

• this paper introduce Either too easy or too old Detector for me… Rather read the latest paper.
  • M2det
  • EfficientDet
  • YOLOv4
  • RefineNet

0. Abstract

1. a comprehensive survey of the recent achievements
2. More than 300 research contributions
3. frameworks/ representation/ proposal generation/ context modeling/ training strategies/ evaluation metrics
4. promising directions for future research

1. introduction

• Deep Convolutional Neural Network (DCNN) called AlexNet in 2012
• Over the past 5 years, this article attempts to track in order to gain a clearer picture

• popular datasets/ evaluation metrics/ context modeling/ detection proposal methods.

• many object detection previous surveys (refer to reference this paper later)
  • pedestrian detection
  • face detection
  • vehicle detection
  • traffic sign detection
  • generic object detection
• we have limited our focus to top journal and conference papers. and limited picture detection not Video.
• follows
  1. 20-years are summarized
  2. A brief introduction to deep learning
  3. Popular datasets and evaluation criteria
  4. the milestone object detection frameworks
  5. state-of-the- art performance
  6. future research directions

2. Generic Object Detection

2.1 The Problem

• future challenges will move to the pixel level object detection.

2.2 Challenges

• Accuracy Related Challenges
  • intra-class variations : color, texture, material, shape, size, poses, clothes
  • Imaging condition variations : lighting, physical location, weather, backgrounds
  • 20, 200, 91 object classes / VOC, ILSVRC, COCO is much smaller than can be recognized by humans.
• Efficiency and Scalability Related Challenges
  • mobile/wearable devices have limited computational capabilities and storage space
  • A further challenge is that of scalability : unseen objects, unknown situations, it may become impossible to annotate them manually, forcing a reliance on weakly supervised strategies.

2.3 Progress in the Past 2 Decades

• 
• accurate annotations are labor intensive to obtain
• Ability to detect many object categories matches that of humans(인간 같은) 3000~3000 categories is undoubtedly an unresolved problem so far.

3. A Brief Introduction to Deep Learning

• pass

4.1 Dataset

• PASCAL VOC
• ImageNet
• MS COCO
• Places
• Open Images

4.2 Evaluation Criteria

• Frames Per Second (FPS)
• precision, and recall.
• Average Precision (AP) -> over all object categories, the mean AP (mAP)

5. Basic Frameworks

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