前言

Object detection is a core problem in computer vision.

记录读过的 PDF 版本的 目标检测 论文.

操作系统:Windows 10 专业版

参考文档

  1. 目标检测中的“神奇指南”——平均精度(mAP)

  2. 目标检测之YOLO算法:YOLOv1,YOLOv2,YOLOv3,TinyYOLO,YOLOv4,YOLOv5,YOLObile,YOLOF,YOLOX详解

  3. MMDetection框架入门教程(完全版)

  4. 目标检测究竟发展到了什么程度?

  5. 一文览尽目标检测开源框架

YOLO V1

论文: You Only Look Once: Unified, Real-Time Object Detection .

Abstract

We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance. Our unified architecture is extremely fast. Our base YOLO model processes images in real-time at 45 frames per second. A smaller version of the network, Fast YOLO, processes an astounding 155 frames per second while still achieving double the mAP of other real-time detectors. Compared to state-of-the-art detection systems, YOLO makes more localization errors but is less likely to predict false positives on background. Finally, YOLO learns very general representations of objects. It outperforms other detection methods, including DPM and R-CNN, when generalizing from natural images to other domains like artwork.

项目地址:

  1. https://pjreddie.com/darknet/yolo/ .

  2. https://github.com/pjreddie/darknet .

论文地址:

  1. https://openaccess.thecvf.com/content_cvpr_2016/html/Redmon_You_Only_Look_CVPR_2016_paper.html .

  2. https://arxiv.org/abs/1506.02640 .

PDF 地址:

  1. https://openaccess.thecvf.com/content_cvpr_2016/papers/Redmon_You_Only_Look_CVPR_2016_paper.pdf .

  2. https://arxiv.org/pdf/1506.02640.pdf .

  3. https://pjreddie.com/media/files/papers/yolo_1.pdf .

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Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 779-788

@InProceedings{Redmon_2016_CVPR,
author = {Redmon, Joseph and Divvala, Santosh and Girshick, Ross and Farhadi, Ali},
title = {You Only Look Once: Unified, Real-Time Object Detection},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2016}
}

You Only Look Once: Unified, Real-Time Object Detection
Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi
CVPR 2016, OpenCV People's Choice Award

@misc{redmon2016look,
title={You Only Look Once: Unified, Real-Time Object Detection},
author={Joseph Redmon and Santosh Divvala and Ross Girshick and Ali Farhadi},
year={2016},
eprint={1506.02640},
archivePrefix={arXiv},
primaryClass={cs.CV}
}

笔记 PDF 地址: https://cdn.jsdelivr.net/gh/LuYF-Lemon-love/susu-object-detection-papers/read/01-YOLO-V1.pdf .


结语

第六十四篇博文写完,开心!!!!

今天,也是充满希望的一天。