Object Detection Methods using Deep Learning

Oct 4, 2016Download as PPTX, PDF8 likes2,513 views

The document discusses object detection techniques including R-CNN, SPPnet, Fast R-CNN, and Faster R-CNN. R-CNN uses region proposals and CNN features to classify each region. SPPnet improves efficiency by computing CNN features once for the whole image. Fast R-CNN further improves efficiency by sharing computation and using a RoI pooling layer. Faster R-CNN introduces a region proposal network to generate proposals, achieving end-to-end training. The techniques showed improved accuracy and processing speed over prior methods.

Object detection
Sungjoon Choi
(sungjoon.choi@cpslab.snu.ac.kr)

R-CNN
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Image Regions Resize Convolution
Features
Classify

SPP net
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Image Convolution Features SPPRegions Classify

Fast R-CNN
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Image
Convolution Features
Regions
RoI Pooling
Layer
Class Label
Confidence
RoI Pooling
Layer
Class Label
Confidence

R-CNN vs. SPP net vs. Fast R-CNN
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R-CNN SPP net
Fast R-CNN

Faster R-CNN
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Image Fully Convolutional
Features
Bounding Box
Regression
BB Classification
FastR-CNN

R-CNN vs. SPP net vs. Fast R-CNN
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Object Detection Methods using Deep Learning

Real-time object detection coz YOLO! by Shagufta Gurmukhdas You Only Look Once is a state-of-the-art, high speed real-time object detection algorithm. It looks at the whole image at test time so its predictions are informed by global context in the image. This talk teaches you to develop your own application to detect and classify objects in images & videos. 1.Intro to YOLO algorithm 2. Image detection on video with YOLO 3. Processing images in Python, adding bounding boxes and labels 4. Processing complete videos in Python in the similar way as the previous section 5. Processing real time video from webcam

camera-based Lane detection by deep learningYu Huang

150807 Fast R-CNNJunho Cho

Fast R-CNN is a method that improves object detection speed and accuracy over previous methods like R-CNN and SPPnet. It uses a region of interest pooling layer and multi-task loss to jointly train a convolutional neural network for classification and bounding box regression in a single stage of training. This allows the entire network to be fine-tuned end-to-end for object detection, resulting in faster training and testing compared to previous methods while achieving state-of-the-art accuracy on standard datasets. Specifically, Fast R-CNN trains 9x faster than R-CNN and runs 200x faster at test time.

Focal loss for dense object detectionDaeHeeKim31

세미나 발표자료 참고자료: • https://hoya012.github.io/blog/Tutorials-of-Object-Detection-Using-Deep-Learning-first-object-detectionusing-deep-learning/ • https://arxiv.org/pdf/1708.02002.pdf#page=10&zoom=100,66,480 • https://arxiv.org/pdf/1506.01497.pdf • https://towardsdatascience.com/yolo-v3-object-detection-53fb7d3bfe6b

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The document summarizes the You Only Look Once (YOLO) object detection method. YOLO frames object detection as a single regression problem to directly predict bounding boxes and class probabilities from full images in one pass. This allows for extremely fast detection speeds of 45 frames per second. YOLO uses a feedforward convolutional neural network to apply a single neural network to the full image. This allows it to leverage contextual information and makes predictions about bounding boxes and class probabilities for all classes with one network.

Faster R-CNN: Towards real-time object detection with region proposal network...Universitat Politècnica de Catalunya

Slides by Amaia Salvador at the UPC Computer Vision Reading Group. Source document on GDocs with clickable links: https://docs.google.com/presentation/d/1jDTyKTNfZBfMl8OHANZJaYxsXTqGCHMVeMeBe5o1EL0/edit?usp=sharing Based on the original work: Ren, Shaoqing, Kaiming He, Ross Girshick, and Jian Sun. "Faster R-CNN: Towards real-time object detection with region proposal networks." In Advances in Neural Information Processing Systems, pp. 91-99. 2015.

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YOLO (You Only Look Once) is a real-time object detection system that frames object detection as a regression problem. It uses a single neural network that predicts bounding boxes and class probabilities directly from full images in one evaluation. This approach allows YOLO to process images and perform object detection over 45 frames per second while maintaining high accuracy compared to previous systems. YOLO was trained on natural images from PASCAL VOC and can generalize to new domains like artwork without significant degradation in performance, unlike other methods that struggle with domain shift.

Faster R-CNNanna8885

This document summarizes the Faster R-CNN object detection framework. It inserts a Region Proposal Network after the last convolutional layer to directly produce region proposals rather than using external proposals. The RPN classifies anchors as object or not and regresses bounding box offsets. Proposals are then fed into Fast R-CNN for classification and further regression. Experiments show Faster R-CNN achieves real-time speeds of 0.2 seconds per image while maintaining accuracy, representing a 250x speedup over R-CNN and 25x over Fast R-CNN.

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Mask R-CNN is an algorithm for instance segmentation that builds upon Faster R-CNN by adding a branch for predicting masks in parallel with bounding boxes. It uses a Feature Pyramid Network to extract features at multiple scales, and RoIAlign instead of RoIPool for better alignment between masks and their corresponding regions. The architecture consists of a Region Proposal Network for generating candidate object boxes, followed by two branches - one for classification and box regression, and another for predicting masks with a fully convolutional network using per-pixel sigmoid activations and binary cross-entropy loss. Mask R-CNN achieves state-of-the-art performance on standard instance segmentation benchmarks.

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Object detection is an important computer vision technique with applications in several domains such as autonomous driving, personal and industrial robotics. The below slides cover the history of object detection from before deep learning until recent research. The slides aim to cover the history and future directions of object detection, as well as some guidelines for how to choose which type of object detector to use for your own project.

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https://telecombcn-dl.github.io/2017-dlcv/ Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.

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http://imatge-upc.github.io/telecombcn-2016-dlcv/ Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.

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The document describes a project that aims to develop a mobile application for real-time object and pose detection. The application will take in a real-time image as input and output bounding boxes identifying the objects in the image along with their class. The methodology involves preprocessing the image, then using the YOLO framework for object classification and localization. The goals are to achieve high accuracy detection that can be used for applications like vehicle counting and human activity recognition.

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The document discusses different approaches to object detection in images using deep learning. It begins with describing detection as classification, where an image is classified into categories for what objects are present. It then discusses approaches that involve separating detection into a classification head and localization head. The document also covers improvements like R-CNN which uses region proposals to first generate candidate object regions before running classification and bounding box regression on those regions using CNN features. This helps address issues with previous approaches like being too slow when running the CNN over the entire image at multiple locations and scales.

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This document provides an introduction to deep reinforcement learning. It begins with an overview of reinforcement learning and its key characteristics such as using reward signals rather than supervision and sequential decision making. The document then covers the formulation of reinforcement learning problems using Markov decision processes and the typical components of an RL agent including policies, value functions, and models. It discusses popular RL algorithms like Q-learning, deep Q-networks, and policy gradient methods. The document concludes by outlining some potential applications of deep reinforcement learning and recommending further educational resources.

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This document discusses the real-time object detection method YOLO (You Only Look Once). YOLO divides an image into grids and predicts bounding boxes and class probabilities for each grid cell. It sees the full image at once rather than using a sliding window approach. This allows it to detect objects in one pass of the neural network, making it very fast compared to other methods. YOLO is also accurate, achieving a high mean average precision. However, it can struggle to precisely localize small objects and objects that appear in dense groups.

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