Object Detection

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Introduction to Image Datasets, Object Detection, Object Tracking, and its Applications.

Datasets

COCO Dataset

  • Object segmentation
  • Recognition in context
  • Superpixel stuff segmentation
  • 330K images (>200K labeled)
  • 1.5 million object instances
  • 80 object categories
  • 91 stuff categories
  • 5 captions per image
  • 250,000 people with keypoints

Open Images Dataset

  • 15,851,536 boxes on 600 categories
  • 2,785,498 instance segmentations on 350 categories
  • 3,284,280 relationship annotations on 1,466 relationships
  • 66,391,027 point-level annotations on 5,827 classes
  • 61,404,966 image-level labels on 20,638 classes
  • Extension - 478,000 crowdsourced images with 6,000+ categories

Roboflow

https://universe.roboflow.com

labelme

pip install labelme
labelme pic123.jpg

Labelme2YOLO

pip install labelme2yolo

  • Convert JSON files, split training and validation dataset by –val_size
    python labelme2yolo.py --json_dir /home/username/labelme_json_dir/ --val_size 0.2

LabelImg

pip install labelImg

labelImg
labelImg [IMAGE_PATH] [PRE-DEFINED CLASS FILE]

VOC .xml convert to YOLO .txt

cd ~/tf/raccoon/annotations python ~/tf/xml2yolo.py

YOLO Annotation formats (.txt)

class_num x, y, w, h

0 0.5222826086956521 0.5518115942028986 0.025 0.010869565217391304
0 0.5271739130434783 0.5057971014492754 0.013043478260869565 0.004347826086956522

Object Detection

Object Detection Landscape

Blog: The Object Detection Landscape: Accuracy vs Runtime

R-CNN, Fast R-CNN, Faster R-CNN

Blog: 目標檢測

  • R-CNN首先使用Selective search提取region proposals（候選框）；然後用Deep Net（Conv layers）進行特徵提取；最後對候選框類別分別採用SVM進行類別分類，採用迴歸對bounding box進行調整。其中每一步都是獨立的。
  • Fast R-CNN在R-CNN的基礎上，提出了多任務損失(Multi-task Loss), 將分類和bounding box迴歸作爲一個整體任務進行學習；另外，通過ROI Projection可以將Selective Search提取出的ROI區域（即：候選框Region Proposals）映射到原始圖像對應的Feature Map上，減少了計算量和存儲量，極大的提高了訓練速度和測試速度。
  • Faster R-CNN則是在Fast R-CNN的基礎上，提出了RPN網絡用來生成Region Proposals。通過網絡共享將提取候選框與目標檢測結合成一個整體進行訓練，替換了Fast R-CNN中使用Selective Search進行提取候選框的方法，提高了測試過程的速度。

R-CNN

Paper: arxiv.org/abs/1311.2524

Fast R-CNN

Paper: arxiv.org/abs/1504.08083
Github: faster-rcnn

Faster R-CNN

Paper: arxiv.org/abs/1506.01497
Github: faster_rcnn, py-faster-rcnn

Blog: [物件偵測] S3: Faster R-CNN 簡介

  • RPN是一個要提出proposals的小model，而這個小model需要我們先訂出不同尺度、比例的proposal的邊界匡的雛形。而這些雛形就叫做anchor。

  • RPN的上路是負責判斷anchor之中有無包含物體的機率，因此，1×1的卷積深度就是9種anchor，乘上有無2種情況，得18。而下路則是負責判斷anchor的x, y, w, h與ground truth的偏差量(offsets)，因此9種anchor，乘上4個偏差量(dx, dy, dw, dh)，得卷積深度為36。

Mask R-CNN

Paper: arxiv.org/abs/1703.06870

<img width="50%" height="50%" src="https://miro.medium.com/max/2000/0*-tQsWmjcPhVfwRZ4"

Blog: [物件偵測] S9: Mask R-CNN 簡介

Code: matterport/Mask_RCNN

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SSD: Single Shot MultiBox Detector

Paper: arxiv.org/abs/1512.02325
Blog: Understanding SSD MultiBox — Real-Time Object Detection In Deep Learning
使用神經網絡（VGG-16）提取feature map後進行分類和回歸來檢測目標物體。 Code: pierluigiferrari/ssd_keras

RetinaNet

Paper: Focal Loss for Dense Object Detection
Code: keras-retinanet
Blog: RetinaNet 介紹 從左到右分別用上了

  • 殘差網路(Residual Network ResNet)
  • 特徵金字塔(Feature Pyramid Network FPN)
  • 類別子網路(Class Subnet)
  • 框子網路(Box Subnet)
  • 以及Anchors

CornerNet

Paper: CornerNet: Detecting Objects as Paired Keypoints
Code: princeton-vl/CornerNet

CenterNet

Paper: CenterNet: Keypoint Triplets for Object Detection
Code: xingyizhou/CenterNet

EfficientDet

Paper: arxiv.org/abs/1911.09070
Code: google efficientdet

Kaggle: rkuo2000/efficientdet-gwd

YOLO- You Only Look Once

Code: pjreddie/darknet

YOLOv1 : mapping bounding box

YOLOv2 : anchor box proportional to K-means

YOLOv3 : Darknet-53 + FPN

YOLObile

Paper: arxiv.org/abs/2009.05697
Code: nightsnack/YOLObile

YOLOv4

Paper: YOLOv4: Optimal Speed and Accuracy of Object Detection

  • YOLOv4 = YOLOv3 + CSPDarknet53 + SPP + PAN + BoF + BoS
  • CSP
  • PANet

Code: AlexeyAB/darknet
Code: WongKinYiu/PyTorch_YOLOv4

YOLOv5

Code: ultralytics/yolov5/

< img src="https://user-images.githubusercontent.com/26833433/127574988-6a558aa1-d268-44b9-bf6b-62d4c605cc72.jpg">

< img src="https://user-images.githubusercontent.com/26833433/136901921-abcfcd9d-f978-4942-9b97-0e3f202907df.png">

Scaled-YOLOv4

Paper: arxiv.org/abs/2011.08036
Code: WongKinYiu/ScaledYOLOv4

YOLOR : You Only Learn One Representation

Paper: arxiv.org/abs/2105.04206
Code: WongKinYiu/yolor

YOLOX

Paper: arxiv.org/abs/2107.08430
Code: Megvii-BaseDetection/YOLOX

CSL-YOLO

Paper: arxiv.org/abs/2107.04829
Code: D0352276/CSL-YOLO

PP-YOLOE

Paper: PP-YOLOE: An evolved version of YOLO
Code: PaddleDetection
Kaggle: rkuo2000/pp-yoloe

YOLOv6

Blog: YOLOv6：又快又准的目标检测框架开源啦
Code: meituan/YOLOv6

YOLOv7

Paper: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

  • Extended efficient layer aggregation networks
  • Model scaling for concatenation-based models
  • Planned re-parameterized convolution
  • Coarse for auxiliary and fine for lead head label assigner

Code: WongKinYiu/yolov7

YOLOv8

Ultralytics YOLOv8 is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility. YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection and tracking, instance segmentation, image classification and pose estimation tasks.

Blog: Dive into YOLOv8
Paper: Real-Time Flying Object Detection with YOLOv8

Code: https://github.com/ultralytics/ultralytics
Kaggle:

UAV-YOLOv8

Paper: UAV-YOLOv8: A Small-Object-Detection Model Based on Improved YOLOv8 for UAV Aerial Photography Scenarios

YOLOv8 Aerial Sheep Detection and Counting

Code: https://github.com/monemati/YOLOv8-Sheep-Detection-Counting

YOLOv8 Drone Surveillance

Code: https://github.com/ni9/Object-Detection-From-Drone-For-Surveillance

YOLOv9

Paper: YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information

Blog: YOLOv9: Advancing the YOLO Legacy
Programmable Gradient Information (PGI) GELAN architecture

Code: https://github.com/WongKinYiu/yolov9

YOLOv10

Paper: YOLOv10: Real-Time End-to-End Object Detection
Code: https://github.com/THU-MIG/yolov10

YOLOv1 ~ YOLOv10

Paper: YOLOv1 to YOLOv10: The fastest and most accurate real-time object detection systems

YOLOV11

Github: https://github.com/ultralytics/ultralytics

Trash Detection

Localize and Classify Wastes on the Streets

Paper: arxiv.org/abs/1710.11374
Model: GoogLeNet

Street Litter Detection

Code: isaychris/litter-detection-tensorflow

TACO: Trash Annotations in Context

Paper: arxiv.org/abs/2003.06875
Code: pedropro/TACO
Model: Mask R-CNN

Marine Litter Detection

Paper: arxiv.org/abs/1804.01079
Dataset: Deep-sea Debris Database

Marine Debris Detection

Ref. Detect Marine Debris from Aerial Imagery
Code: yhoztak/object_detection
Model: RetinaNet

UDD dataset

Paper: A New Dataset, Poisson GAN and AquaNet for Underwater Object Grabbing
Dataset: UDD_Official
Concretely, UDD consists of 3 categories (seacucumber, seaurchin, and scallop) with 2,227 images

Detecting Underwater Objects (DUO)

Paper: A Dataset And Benchmark Of Underwater Object Detection For Robot Picking
Dataset: DUO

Other Applications

Satellite Image Deep Learning

T-CNN : Tubelets with CNN

Paper: arxiv.org/abs/1604.02532
Blog: 人工智慧在太空的應用

Swimming Pool Detection

Dataset: Aerial images of swimming pools
Kaggle: Evaluation Efficientdet - Swimming Pool Detection

Identify Military Vehicles in Satellite Imagery

Blog: Identify Military Vehicles in Satellite Imagery with TensorFlow
Dataset: Moving and Stationary Target Acquisition and Recognition (MSTAR) Dataset
Code: Target Recognition in Sythentic Aperture Radar Imagery Using Deep Learning
script.ipynb

YOLOv5 Detect

detect image / video

YOLOv5 Elephant

train YOLOv5 for detecting elephant (dataset from OpenImage V6)

BCCD Dataset

3 classes: RBC (Red Blood Cell), WBC (White Blood Cell), Platelets (血小板)
Kaggle: https://www.kaggle.com/datasets/surajiiitm/bccd-dataset

Face Mask Dataset

Kaggle: https://kaggle.com/rkuo2000/yolov5-facemask

Traffic Analysis

Kaggle: https://kaggle.com/rkuo2000/yolov5-traffic-analysis

Global Wheat Detection

Kaggle: https://www.kaggle.com/rkuo2000/yolov5-global-wheat-detection ![](https://github.com/rkuo2000/AI-course/blob/main/images/YOLOv5_GWD.jpg?raw=true) **Kaggle:** [https://www.kaggle.com/rkuo2000/efficientdet-gwd](https://www.kaggle.com/rkuo2000/efficientdet-gwd)
![](https://github.com/rkuo2000/AI-course/blob/main/images/EfficientDet_GWD.png?raw=true)

Mask R-CNN

Kaggle: rkuo2000/mask-rcnn

Mask R-CNN transfer learning

Kaggle: Mask RCNN transfer learning

Objectron

Kaggle: rkuo2000/mediapipe-objectron

OpenCV-Python play GTA5

Ref. Reading game frames in Python with OpenCV - Python Plays GTA V
Code: Sentdex/pygta5

Steel Defect Detection

Dataset: Severstal: Steel Defect Detection
Kaggle: https://www.kaggle.com/code/jaysmit/u-net (Keras UNet)

PCB Defect Detection

Dataset: HRIPCB dataset (dropbox)

Pothole Detection

Blog: Pothole Detection using YOLOv4
Code: yolov4_pothole_detection.ipynb
Kaggle: YOLOv7 Pothole Detection

Car Breaking Detection

Code: YOLOv7 Braking Detection

Steel Defect Detection

Dataset: Severstal: Steel Defect Detection

Steel Defect Detection using UNet

Kaggle: https://www.kaggle.com/code/jaysmit/u-net (Keras UNet)
Kaggle: https://www.kaggle.com/code/myominhtet/steel-defection (pytorch UNet

Steel-Defect Detection Using CNN

Code: https://github.com/himasha0421/Steel-Defect-Detection

MSFT-YOLO

Paper: MSFT-YOLO: Improved YOLOv5 Based on Transformer for Detecting Defects of Steel Surface

PCB Datasets

PCB Defect Detection

Paper: PCB Defect Detection Using Denoising Convolutional Autoencoders

PCB Defect Classification

Dataset: HRIPCB dataset (dropbox)
印刷电路板（PCB）瑕疵数据集。它是一个公共合成PCB数据集，包含1386张图像，具有6种缺陷（漏孔、鼠咬、开路、短路、杂散、杂铜），用于图像检测、分类和配准任务。
Paper: End-to-end deep learning framework for printed circuit board manufacturing defect classification

Object Tracking Datasets

Paper: Deep Learning in Video Multi-Object Tracking: A Survey

Multiple Object Tracking (MOT)

MOT-16

Under-water Ojbect Tracking (UOT)

Paper: Underwater Object Tracking Benchmark and Dataset
UOT32
UOT100

Re3 : Real-Time Recurrent Regression Networks for Visual Tracking of Generic Objects

Paper: arxiv.org/abs/1705.06368
Code: moorejee/Re3

Deep SORT

Paper: Simple Online and Realtime Tracking with a Deep Association Metric
Code: https://github.com/nwojke/deep_sort

SiamCAR

Paper: arxiv.org/abs/1911.07241
Code: ohhhyeahhh/SiamCAR

YOLOv5 + DeepSort

Code: HowieMa/DeepSORT_YOLOv5_Pytorch

Yolov5 + StrongSORT with OSNet

Code: https://github.com/mikel-brostrom/Yolov5_StrongSORT_OSNet

BoxMOT

Code: BoxMOT: pluggable SOTA tracking modules for segmentation, object detection and pose estimation models

SiamBAN

Paper: arxiv.org/abs/2003.06761
Code: hqucv/siamban
Blog: [CVPR2020][SiamBAN] Siamese Box Adaptive Network for Visual Tracking

FairMOT

Paper: FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
Code: ifzhang/FairMOT

3D-ZeF

Paper: arxiv.org/abs/2006.08466
Code: mapeAAU/3D-ZeF

ByteTrack

Paper: ByteTrack: Multi-Object Tracking by Associating Every Detection Box
Code: https://github.com/ifzhang/ByteTrack

OC-SORT

Paper: Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking
Code: https://github.com/noahcao/OC_SORT

Deep OC-SORT

Paper: Deep OC-SORT: Multi-Pedestrian Tracking by Adaptive Re-Identification
Code: https://github.com/GerardMaggiolino/Deep-OC-SORT

Track Anything

Paper: Track Anything: Segment Anything Meets Videos
Cpde: https://github.com/gaomingqi/Track-Anything

YOLOv8 + DeepSORT

Code: https://github.com/MuhammadMoinFaisal/YOLOv8-DeepSORT-Object-Tracking

MeMOTR

Paper: MeMOTR: Long-Term Memory-Augmented Transformer for Multi-Object Tracking
Code: https://github.com/MCG-NJU/MeMOTR

Hybrid-SORT

Paper: Hybrid-SORT: Weak Cues Matter for Online Multi-Object Tracking
Code: https://github.com/ymzis69/HybridSORT

MOTIP

Paper: Multiple Object Tracking as ID Prediction
Code: https://github.com/MCG-NJU/MOTIP

LITE

Paper: LITE: A Paradigm Shift in Multi-Object Tracking with Efficient ReID Feature Integration
Code: https://github.com/Jumabek/LITE
The Lightweight Integrated Tracking-Feature Extraction (LITE) paradigm is introduced as a novel multi-object tracking (MOT) approach.



This site was last updated November 15, 2024.