Object Detection (D2L5 Insight@DCU Machine Learning Workshop 2017)
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This document provides an overview of object detection techniques including region-based and region-free methods. Region-based methods like R-CNN, Fast R-CNN, and Faster R-CNN first generate region proposals then extract features from those regions to classify and regress bounding boxes. Region-free methods like YOLO, YOLOv2, and SSD predict bounding boxes and classifications directly from the image in one pass. Both approaches are trained end-to-end using techniques like RoI pooling and anchor boxes to predict multiple detections. Recent work aims to improve speed and accuracy by generating detections sequentially or using soft NMS instead of hard thresholding.
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![Object Detection as Classification
Classes = [cat, dog, duck]
Cat ? NO
Dog ? NO
Duck? NO
4](https://image.slidesharecdn.com/dlmmdcud2l05objectdetection-170429103817/85/Object-Detection-D2L5-Insight-DCU-Machine-Learning-Workshop-2017-4-320.jpg)
![Classes = [cat, dog, duck]
Cat ? NO
Dog ? NO
Duck? NO
5
Object Detection as Classification](https://image.slidesharecdn.com/dlmmdcud2l05objectdetection-170429103817/85/Object-Detection-D2L5-Insight-DCU-Machine-Learning-Workshop-2017-5-320.jpg)
![Classes = [cat, dog, duck]
Cat ? YES
Dog ? NO
Duck? NO
6
Object Detection as Classification](https://image.slidesharecdn.com/dlmmdcud2l05objectdetection-170429103817/85/Object-Detection-D2L5-Insight-DCU-Machine-Learning-Workshop-2017-6-320.jpg)
![Classes = [cat, dog, duck]
Cat ? NO
Dog ? NO
Duck? NO
7
Object Detection as Classification](https://image.slidesharecdn.com/dlmmdcud2l05objectdetection-170429103817/85/Object-Detection-D2L5-Insight-DCU-Machine-Learning-Workshop-2017-7-320.jpg)
![Region Proposals
Selective Search (SS) Multiscale Combinatorial Grouping (MCG)
[SS] Uijlings et al. Selective search for object recognition. IJCV 2013
[MCG] Arbeláez, Pont-Tuset et al. Multiscale combinatorial grouping. CVPR 2014 13](https://image.slidesharecdn.com/dlmmdcud2l05objectdetection-170429103817/85/Object-Detection-D2L5-Insight-DCU-Machine-Learning-Workshop-2017-13-320.jpg)
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object detection paper reviewYoonho Na - R-CNN was the first CNN model to achieve high performance in object detection. It used a multi-stage pipeline involving region proposals, feature extraction via CNN, and SVM classification. It was slow due to computing CNN features for each region individually.
- Fast R-CNN improved on R-CNN by introducing a ROI pooling layer to share computation and enabling end-to-end training. However, region proposals were still generated externally, slowing down detection.
- Faster R-CNN addressed this by introducing a Region Proposal Network to generate proposals, allowing the entire model to be trained end-to-end. This led to faster and more accurate detection compared to previous models.
- YOLO
Fast methods for deep learning based object detection
Fast methods for deep learning based object detectionBrodmann17 The document discusses improvements made to deep learning based object detection models. It describes issues with the R-CNN model such as slow training and testing speeds. The Fast R-CNN model is then introduced which improves on R-CNN by only extracting features once and using ROI pooling for faster training and testing. Faster R-CNN further improves speed by incorporating a Region Proposal Network to generate proposals instead of using selective search. Finally, SSD is discussed as a single-shot detector that evaluates detections at multiple scales for real-time performance. Throughout, techniques for improving accuracy such as hard negative mining and data augmentation are also covered.
IRJET- Object Detection and Recognition using Single Shot Multi-Box Detector
IRJET- Object Detection and Recognition using Single Shot Multi-Box DetectorIRJET Journal This document summarizes research on object detection and recognition using the Single Shot Multi-Box Detector (SSD) deep learning model. SSD improves on existing object detection systems by eliminating the need for generating object proposals and resampling pixels or features, thereby making detection faster and encapsulating all computation in a single neural network. The researchers applied SSD to standard datasets like PASCAL VOC, COCO, and ILSVRC and achieved competitive accuracy compared to methods using additional proposal steps, with SSD running significantly faster at 59 FPS. Experimental results on PASCAL VOC using SSD achieved a mean average precision of 74.3%, outperforming a comparable Faster R-CNN model.
Cvpr 2017 Summary Meetup
Cvpr 2017 Summary MeetupAmir Alush The document summarizes key highlights and research presented at CVPR 2017, focusing on advances in computer vision and deep learning, particularly in object detection, segmentation, and pose estimation. Notable papers include discussions on YOLO9000, Mask R-CNN, and multi-person pose estimation methods, showcasing improvements in accuracy and efficiency. The document also underscores the need for optimization in deep learning models to enhance performance across various applications.
Brodmann17 CVPR 2017 review - meetup slides
Brodmann17 CVPR 2017 review - meetup slides Brodmann17 The document summarizes Assaf Mushinsky's presentation at CVPR 2017. Some key points:
- He discussed state-of-the-art research in object detection, segmentation, pose estimation, and network architectures from papers presented at CVPR 2017.
- Papers presented efficient object detection methods that improved speed and accuracy trade-offs like YOLO9000 and Feature Pyramid Networks. Mask R-CNN was discussed for instance segmentation and pose estimation.
- New network architectures like Densely Connected Networks, Xception, and ResNeXt were covered that improved accuracy and efficiency over ResNet and Inception.
- The presentation highlighted recent advances in computer vision from the CVPR conference but did not cover older
Improving region based CNN object detector using bayesian optimization
Improving region based CNN object detector using bayesian optimizationAmgad Muhammad The document discusses the evolution of region-based CNN object detectors, highlighting the limitations of previous models like R-CNN, SPP-Net, and Fast R-CNN, particularly in terms of training time and reliance on external region proposal algorithms. It proposes a solution utilizing Bayesian optimization to improve the quality of region proposals in object detection by iteratively refining the locations of proposed regions for better localization. The methodology involves querying the CNN for region scores, fitting a model, and optimizing a utility function to effectively sample new regions that maximize detection accuracy.
Tutorial on Object Detection (Faster R-CNN)
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Anchor free object detection by deep learning
Anchor free object detection by deep learningYu Huang The document provides an overview of various advanced anchor-free object detection methods in deep learning, including UnitBox, DenseBox, YOLO series, CornerNet, and FCOS. Key innovations include the use of intersection over union (IOU) loss for bounding box predictions, detecting objects as points or keypoints, and eliminating the need for predefined anchor boxes for better accuracy and robustness. Techniques like feature pyramid networks and guided anchoring are highlighted to enhance object localization and detection performance across diverse object scales and shapes.
A Brief History of Object Detection / Tommi Kerola
A Brief History of Object Detection / Tommi KerolaPreferred Networks The document presents a comprehensive overview of the evolution of object detection methods, detailing the transition from early handcrafted feature techniques to current deep learning approaches. It highlights key methodologies including two-stage methods, single-shot methods, and anchor-free methods, while also addressing ongoing challenges such as the reliance on non-maximum suppression and suboptimal bounding box representations. The conclusion emphasizes the significant advancements brought about by deep learning and suggests that anchor-free methods are increasingly favorable for fast and accurate object detection.
“Understanding DNN-Based Object Detectors,” a Presentation from Au-Zone Techn...
“Understanding DNN-Based Object Detectors,” a Presentation from Au-Zone Techn...Edge AI and Vision Alliance The document provides an overview of deep learning based object detection models. It discusses early approaches like R-CNN, Fast R-CNN, and Faster R-CNN, as well as more recent single-shot detectors like YOLO, SSD, RetinaNet, and CenterNet. It covers performance metrics like mean average precision (mAP) and compares the speed and accuracy of different models. The document concludes by outlining general guidelines for choosing an object detection model based on priorities like accuracy, speed, model size, and portability.
Object Detection An Overview
Object Detection An Overviewijtsrd The document provides an overview of an object detection algorithm aimed at accurately identifying and classifying objects in video frames, which is critical for applications like autonomous driving. It discusses the motivation behind creating high-performance detection models, the limitations faced such as occlusion and illumination issues, and evaluates various deep learning approaches including one-stage (YOLO, SSD) and two-stage (R-CNN, Fast R-CNN, Faster R-CNN) methods. The proposed model demonstrates effective results using the COCO dataset, achieving high confidence in object detection while still needing improvements in accuracy.
YOLO_review.pptxThis is a test document that is used to satisfy the requireme...
YOLO_review.pptxThis is a test document that is used to satisfy the requireme...gaojinming318 This is a test document that is used to satisfy the requirement to upload two documents.
Object Detection is a very powerful field.pptx
Object Detection is a very powerful field.pptxusmanyaseen16 The document provides an overview of the evolution of object detection methodologies, tracing the shift from hand-crafted feature detectors to deep learning approaches like OverFeat, R-CNN, and YOLO. It discusses the transition from two-stage processes that relied on region proposals to more streamlined one-stage detection methods that prioritize speed while sacrificing some accuracy. Key innovations include the introduction of Region Proposal Networks in Faster R-CNN, and the Single Shot Multibox Detector (SSD), which employs multi-scale detection and default bounding boxes to improve performance and efficiency.
IISc Internship Report
IISc Internship ReportHarshilJain26 This document summarizes an academic paper that proposes a method for incrementally training object detection models to classify unseen object classes in real-time. It begins by providing background on object detection techniques like YOLO and SSD that can perform detection in a single pass. The paper aims to improve these single-shot detectors through incremental learning to classify new object classes without retraining the entire model from scratch. It conducted experiments on YOLO and VGG16 to investigate how well they can classify objects from unseen classes and whether their performance is affected by factors like background, bounding box size, or network architecture. The goal is to develop a more robust object detection method that can easily adapt to new classes of objects in real-time applications.
Faster R-CNN - PR012
Faster R-CNN - PR012Jinwon Lee Faster R-CNN improves object detection by introducing a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. The RPN slides over feature maps and predicts object bounds and objectness at each position. During training, anchors are assigned positive or negative labels based on Intersection over Union with ground truth boxes. Faster R-CNN runs the RPN in parallel with Fast R-CNN for detection, end-to-end in a single network and stage. This achieves state-of-the-art object detection speed and accuracy while eliminating computationally expensive selective search for proposals.
IRJET- Weakly Supervised Object Detection by using Fast R-CNN
IRJET- Weakly Supervised Object Detection by using Fast R-CNNIRJET Journal This document summarizes a research paper that proposes using Fast R-CNN (Region-based Convolutional Neural Network) for weakly supervised object detection. The researchers introduce a Region Proposal Network (RPN) that is trained end-to-end to generate high-quality region proposals, which are then used by Fast R-CNN for detection. This approach helps overcome limitations of previous Multiple Instance Learning frameworks that provide false positives during training. The RPN represents the input image as a "bag of boxes" and iteratively selects a set of images and boxes that are more reliable.
“Understanding, Selecting and Optimizing Object Detectors for Edge Applicatio...
“Understanding, Selecting and Optimizing Object Detectors for Edge Applicatio...Edge AI and Vision Alliance
Faster R-CNN: Towards real-time object detection with region proposal network...
Faster R-CNN: Towards real-time object detection with region proposal network...Universitat Politècnica de Catalunya
“Understanding DNN-Based Object Detectors,” a Presentation from Au-Zone Techn...
“Understanding DNN-Based Object Detectors,” a Presentation from Au-Zone Techn...Edge AI and Vision Alliance
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Object Detection (D2L5 Insight@DCU Machine Learning Workshop 2017)
- 1. Amaia Salvador [email protected] PhD Candidate Universitat Politècnica de Catalunya Object Detection Day 2 Lecture 5
- 2. Object Detection CAT, DOG, DUCK The task of assigning a label and a bounding box to all objects in the image 2
- 3. Object Detection: Datasets 3 20 categories 6k training images 6k validation images 10k test images 200 categories 456k training images 60k validation + test images 80 categories 200k training images 60k val + test images
- 4. Object Detection as Classification Classes = [cat, dog, duck] Cat ? NO Dog ? NO Duck? NO 4
- 5. Classes = [cat, dog, duck] Cat ? NO Dog ? NO Duck? NO 5 Object Detection as Classification
- 6. Classes = [cat, dog, duck] Cat ? YES Dog ? NO Duck? NO 6 Object Detection as Classification
- 7. Classes = [cat, dog, duck] Cat ? NO Dog ? NO Duck? NO 7 Object Detection as Classification
- 8. Problem: Too many positions & scales to test Solution: If your classifier is fast enough, go for it 8 Object Detection as Classification
- 9. HOG: Histogram of Oriented Gradients Dalal and Triggs. Histograms of Oriented Gradients for Human Detection. CVPR 2005 9
- 10. Deformable Part Model Felzenszwalb et al, Object Detection with Discriminatively Trained Part Based Models, PAMI 2010 10
- 11. Object Detection with ConvNets? Convnets are computationally demanding. We can’t test all positions & scales ! Solution: Look at a tiny subset of positions. Choose them wisely :) 11
- 12. Region Proposals ● Find “blobby” image regions that are likely to contain objects ● “Class-agnostic” object detector ● Look for “blob-like” regions Slide Credit: CS231n 12
- 13. Region Proposals Selective Search (SS) Multiscale Combinatorial Grouping (MCG) [SS] Uijlings et al. Selective search for object recognition. IJCV 2013 [MCG] Arbeláez, Pont-Tuset et al. Multiscale combinatorial grouping. CVPR 2014 13
- 14. Object Detection with Convnets: R-CNN Girshick et al. Rich feature hierarchies for accurate object detection and semantic segmentation. CVPR 2014 14
- 15. R-CNN Girshick et al. Rich feature hierarchies for accurate object detection and semantic segmentation. CVPR 2014 1. Train network on proposals 2. Post-hoc training of SVMs & Box regressors on fc7 features 15
- 16. R-CNN 16 We expect: We get:
- 17. R-CNN Girshick et al. Rich feature hierarchies for accurate object detection and semantic segmentation. CVPR 2014 1. Train network on proposals 2. Post-hoc training of SVMs & Box regressors on fc7 features 3. Non Maximum Suppression + score threshold 17
- 18. R-CNN Girshick et al. Rich feature hierarchies for accurate object detection and semantic segmentation. CVPR 2014 18
- 19. R-CNN: Problems 1. Slow at test-time: need to run full forward pass of CNN for each region proposal 2. SVMs and regressors are post-hoc: CNN features not updated in response to SVMs and regressors 3. Complex multistage training pipeline Slide Credit: CS231n 19
- 20. Fast R-CNN Girshick Fast R-CNN. ICCV 2015 Solution: Share computation of convolutional layers between region proposals for an image R-CNN Problem #1: Slow at test-time: need to run full forward pass of CNN for each region proposal 20
- 21. Fast R-CNN: Sharing features Hi-res input image: 3 x 800 x 600 with region proposal Convolution and Pooling Hi-res conv features: C x H x W with region proposal Fully-connected layers Max-pool within each grid cell RoI conv features: C x h x w for region proposal Fully-connected layers expect low-res conv features: C x h x w Slide Credit: CS231n 21Girshick Fast R-CNN. ICCV 2015
- 22. Fast R-CNN Solution: Train it all at together E2E R-CNN Problem #2&3: SVMs and regressors are post-hoc. Complex training. 22Girshick Fast R-CNN. ICCV 2015
- 23. Fast R-CNN: End-to-end training 23Girshick Fast R-CNN. ICCV 2015 Predicted class scores True class scores True box coordinates Predicted box coordinates Log loss Smooth L1 loss Only for positive boxes
- 24. Fast R-CNN: Positive / Negative Samples 24Girshick Fast R-CNN. ICCV 2015 Positive samples are defined as those whose IoU overlap with a ground-truth bounding box is > 0.5. Negative examples are sampled from those that have a maximum IoU overlap with ground truth in the interval [0.1, 0.5). 25%/75% ratio for positive/negative samples in a minibatch.
- 25. Fast R-CNN Slide Credit: CS231n R-CNN Fast R-CNN Training Time: 84 hours 9.5 hours (Speedup) 1x 8.8x Test time per image 47 seconds 0.32 seconds (Speedup) 1x 146x mAP (VOC 2007) 66.0 66.9 Using VGG-16 CNN on Pascal VOC 2007 dataset Faster! FASTER! Better! 25
- 26. Fast R-CNN: Problem Slide Credit: CS231n R-CNN Fast R-CNN Test time per image 47 seconds 0.32 seconds (Speedup) 1x 146x Test time per image with Selective Search 50 seconds 2 seconds (Speedup) 1x 25x Test-time speeds don’t include region proposals 26
- 27. Faster R-CNN Conv layers Region Proposal Network FC6 Class probabilities FC7 FC8 RPN Proposals RoI Pooling Conv5_3 RPN Proposals 27 Ren et al. Faster R-CNN: Towards real-time object detection with region proposal networks. NIPS 2015 Learn proposals end-to-end sharing parameters with the classification network
- 28. Faster R-CNN Conv layers Region Proposal Network FC6 Class probabilities FC7 FC8 RPN Proposals RoI Pooling Conv5_3 RPN Proposals Fast R-CNN 28 Ren et al. Faster R-CNN: Towards real-time object detection with region proposal networks. NIPS 2015 Learn proposals end-to-end sharing parameters with the classification network
- 29. Region Proposal Network Objectness scores (object/no object) Bounding Box Regression In practice, k = 9 (3 different scales and 3 aspect ratios) 29 Ren et al. Faster R-CNN: Towards real-time object detection with region proposal networks. NIPS 2015
- 30. Region Proposal Network: Loss function 30 Predicted probability of being an object for anchor i i = anchor index in minibatch Coordinates of the predicted bounding box for anchor i Ground truth objectness label True box coordinates Ncls = Number of anchors in minibatch (~ 256) Nreg = Number of anchor locations ( ~ 2400) Log loss Smooth L1 loss In practice = 10, so that both terms are roughly equally balanced
- 31. Region Proposal Network: Positive / Negative Samples 31 An anchor is labeled as positive if: (a) the anchor is the one with highest IoU overlap with a ground-truth box (b) the anchor has an IoU overlap with a ground-truth box higher than 0.7 Negative labels are assigned to anchors with IoU lower than 0.3 for all ground-truth boxes. 50%/50% ratio of positive/negative anchors in a minibatch.
- 32. Faster R-CNN: Training Conv layers Region Proposal Network FC6 Class probabilities FC7 FC8 RPN Proposals RoI Pooling Conv5_3 RPN Proposals 32 Ren et al. Faster R-CNN: Towards real-time object detection with region proposal networks. NIPS 2015 RoI Pooling is not differentiable w.r.t box coordinates. Solutions: ● Alternate training ● Ignore gradient of classification branch w.r.t proposal coordinates ● Make pooling function differentiable
- 33. Faster R-CNN Ren et al. Faster R-CNN: Towards real-time object detection with region proposal networks. NIPS 2015 R-CNN Fast R-CNN Faster R-CNN Test time per image (with proposals) 50 seconds 2 seconds 0.2 seconds (Speedup) 1x 25x 250x mAP (VOC 2007) 66.0 66.9 66.9 Slide Credit: CS231n 33
- 34. Faster R-CNN 34 ● Faster R-CNN is the basis of the winners of COCO and ILSVRC 2015 object detection competitions. He et al. Deep residual learning for image recognition. CVPR 2016
- 35. YOLO: You Only Look Once 35Redmon et al. You Only Look Once: Unified, Real-Time Object Detection, CVPR 2016 Proposal-free object detection pipeline
- 36. YOLO: You Only Look Once Redmon et al. You Only Look Once: Unified, Real-Time Object Detection, CVPR 2016 36
- 37. YOLO: You Only Look Once 37 Each cell predicts: - For each bounding box: - 4 coordinates (x, y, w, h) - 1 confidence value - Some number of class probabilities For Pascal VOC: - 7x7 grid - 2 bounding boxes / cell - 20 classes 7 x 7 x (2 x 5 + 20) = 7 x 7 x 30 tensor = 1470 outputs
- 38. YOLO: Training 38Slide credit: YOLO Presentation @ CVPR 2016 For training, each ground truth bounding box is matched into the right cell
- 39. YOLO: Training 39Slide credit: YOLO Presentation @ CVPR 2016 For training, each ground truth bounding box is matched into the right cell
- 40. YOLO: Training 40Slide credit: YOLO Presentation @ CVPR 2016 Optimize class prediction in that cell: dog: 1, cat: 0, bike: 0, ...
- 41. YOLO: Training 41Slide credit: YOLO Presentation @ CVPR 2016 Predicted boxes for this cell
- 42. YOLO: Training 42Slide credit: YOLO Presentation @ CVPR 2016 Find the best one wrt ground truth bounding box, optimize it (i.e. adjust its coordinates to be closer to the ground truth’s coordinates)
- 43. YOLO: Training 43Slide credit: YOLO Presentation @ CVPR 2016 Increase matched box’s confidence, decrease non-matched boxes confidence
- 44. YOLO: Training 44Slide credit: YOLO Presentation @ CVPR 2016 Increase matched box’s confidence, decrease non-matched boxes confidence
- 45. YOLO: Training 45Slide credit: YOLO Presentation @ CVPR 2016 For cells with no ground truth detections, confidences of all predicted boxes are decreased
- 46. YOLO: Training 46Slide credit: YOLO Presentation @ CVPR 2016 For cells with no ground truth detections: ● Confidences of all predicted boxes are decreased ● Class probabilities are not adjusted
- 47. YOLO: Training, formally 47Slide credit: YOLO Presentation @ CVPR 2016 Bounding box coordinate regression Bounding box score prediction Class score prediction = 1 if box j and cell i are matched together, 0 otherwise = 1 if box j and cell i are NOT matched together = 1 if cell i has an object present
- 48. YOLO: You Only Look Once Redmon et al. You Only Look Once: Unified, Real-Time Object Detection, CVPR 2016 48 Dog Bicycle Car Dining Table Predict class probability for each cell (conditioned on object P(car | object) )
- 49. YOLO: You Only Look Once Redmon et al. You Only Look Once: Unified, Real-Time Object Detection, CVPR 2016 49 + NMS + Score threshold
- 50. SSD: Single Shot MultiBox Detector Liu et al. SSD: Single Shot MultiBox Detector, ECCV 2016 50 Same idea as YOLO, + several predictors at different stages in the network
- 51. SSD: Single Shot MultiBox Detector Liu et al. SSD: Single Shot MultiBox Detector, ECCV 2016 51 Similarly to Faster R-CNN, it uses box anchors to predict box coordinates as displacements
- 52. YOLOv2 52 Redmon & Farhadi. YOLO900: Better, Faster, Stronger. CVPR 2017
- 53. YOLOv2 53 Results on Pascal VOC 2007
- 54. YOLOv2 54 Results on COCO test-dev 2015
- 55. Summary 55 Proposal-based methods ● R-CNN ● Fast R-CNN ● Faster R-CNN ● SPPnet ● R-FCN Proposal-free methods ● YOLO, YOLOv2 ● SSD
- 56. Questions ?
- 57. A note on NMS 57 Tradeoff between recall/precision 0.8 0.6 0.7 Objectness scores
- 58. Soft NMS 58 Bodla, Singh et al. Improving Object Detection With One Line of Code. arXiv Apr 2017 Decay detection scores of contiguous objects instead of setting them to 0
- 59. Avoid NMS: Sequential box prediction 59 Stewart et al. End-To-End People Detection in Crowded Scenes. CVPR 2016 Predict boxes one after the other and learn when to stop