Two stage object detection

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Two stage object detection

Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images. Deep learning techniques have emerged as a powerful strategy for learning feature representations directly from data and have led to remarkable breakthroughs in the field of generic object detection. Given this period of rapid evolution, the goal of this paper is to provide a comprehensive survey of the recent achievements in this field brought about by deep learning techniques.

two stage object detection

More than research contributions are included in this survey, covering many aspects of generic object detection: detection frameworks, object feature representation, object proposal generation, context modeling, training strategies, and evaluation metrics. We finish the survey by identifying promising directions for future research.

As a longstanding, fundamental and challenging problem in computer vision, object detection illustrated in Fig. The goal of object detection is to determine whether there are any instances of objects from given categories such as humans, cars, bicycles, dogs or cats in an image and, if present, to return the spatial location and extent of each object instance e.

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As the cornerstone of image understanding and computer vision, object detection forms the basis for solving complex or high level vision tasks such as segmentation, scene understanding, object tracking, image captioning, event detection, and activity recognition.

Object detection supports a wide range of applications, including robot vision, consumer electronics, security, autonomous driving, human computer interaction, content based image retrieval, intelligent video surveillance, and augmented reality. In particular, these techniques have provided major improvements in object detection, as illustrated in Fig.

As illustrated in Fig. The goal of the second type is to detect usually previously unseen instances of some predefined object categories for example humans, cars, bicycles, and dogs.

Historically, much of the effort in the field of object detection has focused on the detection of a single category typically faces and pedestrians or a few specific categories. In contrast, over the past several years, the research community has started moving towards the more challenging goal of building general purpose object detection systems where the breadth of object detection ability rivals that of humans.

The size of each word is proportional to the frequency of that keyword. We can see that object detection has received significant attention in recent years. Object detection includes localizing instances of a particular object topas well as generalizing to detecting object categories in general bottom.

This survey focuses on recent advances for the latter problem of generic object detection. An overview of recent object detection performance: we can observe a significant improvement in performance measured as mean average precision since the arrival of deep learning in Although tremendous progress has been achieved, illustrated in Fig.

Given the exceptionally rapid rate of progress, this article attempts to track recent advances and summarize their achievements in order to gain a clearer picture of the current panorama in generic object detection. Deep learning allows computational models to learn fantastically complex, subtle, and abstract representations, driving significant progress in a broad range of problems such as visual recognition, object detection, speech recognition, natural language processing, medical image analysis, drug discovery and genomics.

In contrast, although many deep learning based methods have been proposed for object detection, we are unaware of any comprehensive recent survey. A thorough review and summary of existing work is essential for further progress in object detection, particularly for researchers wishing to enter the field. The number of papers on generic object detection based on deep learning is breathtaking. There are so many, in fact, that compiling any comprehensive review of the state of the art is beyond the scope of any reasonable length paper.

As a result, it is necessary to establish selection criteria, in such a way that we have limited our focus to top journal and conference papers. Due to these limitations, we sincerely apologize to those authors whose works are not included in this paper. The main goal of this paper is to offer a comprehensive survey of deep learning based generic object detection techniques, and to present some degree of taxonomy, a high level perspective and organization, primarily on the basis of popular datasets, evaluation metrics, context modeling, and detection proposal methods.

The intention is that our categorization be helpful for readers to have an accessible understanding of similarities and differences between a wide variety of strategies. The proposed taxonomy gives researchers a framework to understand current research and to identify open challenges for future research. The remainder of this paper is organized as follows. A brief introduction to deep learning is given in Sect. Popular datasets and evaluation criteria are summarized in Sect.

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We describe the milestone object detection frameworks in Sect. From Sects. Finally, in Sect.

Lesson 8: Deep Learning Part 2 2018 - Single object detection

Given an image, determine whether or not there are instances of objects from predefined categories usually many categories, e.

A greater emphasis is placed on detecting a broad range of natural categories, as opposed to specific object category detection where only a narrower predefined category of interest e.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Skip to content. Permalink Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

Sign up. Branch: master. Find file Copy path. Cannot retrieve contributors at this time. Raw Blame History. It loads the classifier uses it to perform object detection on a video. It draws boxes and scores around the objects of interest in each frame of the video.

Import packages import os import cv2 import numpy as np import tensorflow as tf import sys import torch import torch. Compose [ transforms. Resize imsizetransforms. CenterCrop imsizetransforms. ToTensortransforms. Normalize [ 0. GraphDef with tf. The score is shown on the result image, together with the class label. You signed in with another tab or window. Reload to refresh your session.

You signed out in another tab or window. Author: Evan Juras. This program uses a TensorFlow-trained classifier to perform object detection.

It draws boxes and scores around the objects of interest in each frame. Some of the code is copied from Google's example at. Import packages. Import utilites. Name of the directory containing the object detection module we're using. Resize imsize. CenterCrop imsize. ToTensor. Grab path to current working directory. Path to frozen detection graph. Path to label map file. Path to video. Number of classes the object detector can identify.Skip to Main Content.

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Email Address. Sign In. Access provided by: anon Sign Out. Salient Object Detection via Two-Stage Graphs Abstract: Despite recent advances made in salient object detection using graph theory, the approach still suffers from accuracy problems when the image is characterized by a complex structure, either in the foreground or background, causing erroneous saliency segmentation.

This fundamental challenge is mainly attributed to the fact that most existing graph-based methods take only the adjacently spatial consistency among graph nodes into consideration. In this paper, we tackle this issue from a coarse-to-fine perspective and propose a two-stage-graphs approach for salient object detection, in which two graphs having the same nodes but different edges are employed.

Specifically, a weighted joint robust sparse representation model, rather than the commonly used manifold ranking model, helps to compute the saliency value of each node in the first-stage graph, thereby providing a saliency map at the coarse level.

In the second-stage graph, along with the adjacently spatial consistency, a new regionally spatial consistency among graph nodes is considered in order to refine the coarse saliency map, assuring uniform saliency assignment even in complex scenes.

One-stage object detection

Particularly, the second stage is generic enough to be integrated in existing salient object detectors, enabling improved performance. Experimental results on benchmark data sets validate the effectiveness and superiority of the proposed scheme over related state-of-the-art methods.

Article :. Date of Publication: 06 April DOI: Need Help?It is discovered that there is extreme foreground-background class imbalance problem in one-stage detector. And it is believed that this is the central cause which makes the performance of one-stage detectors inferior to two-stage detectors. Sik-Ho Tsang Medium. By using focal loss, the total loss can be balanced adaptively between easy samples and hard samples. Sign in. Sik-Ho Tsang Follow. At the second stageclassification is performed for each candidate object location.

Sampling heuristics using fixed foreground-to-background ratioor online hard example mining OHEM to select a small set of anchors e. Thus, there is manageable class balance between foreground and background. One-Stage Detectors. RetinaNet Detector. Feature Pyramid Network FPN is used on top of ResNet for constructing a rich multi-scale feature pyramid from one single resolution input image. Originally, FPN is a two-stage detector which has state-of-the-art results.

Please read my review about FPN if interested. FPN is multiscale, semantically strong at all scales, and fast to compute. There are some modest changes for the FPN here. A pyramid is generated from P3 to P7. Some major changes are: P2 is not used now due to computational reasons. In total, 9 anchors per level. Across levels, scale is covered from 32 to pixels.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

The other different approach skips the region proposal stage and runs detection directly over a dense sampling of possible locations. This is how a one-stage object detection algorithm works. This is faster and simpler, but might potentially drag down the performance a bit. For the last couple years, many results are exclusively measured with the COCO object detection dataset.

Here are the comparison for some key detectors. Higher resolution images for the same model have better mAP but slower to process. Input image resolutions and feature extractors impact speed. Below is the highest and lowest FPS reported by the corresponding papers. Yet, the result below can be highly biased in particular they are measured at different mAP.

two stage object detection

Skip to content. Permalink Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. Branch: master.

Find file Copy path. Raw Blame History. The proposed regions are sparse as the potential bounding box candidates can be infinite. Then a classifier only processes the region candidates.

Review: RetinaNet — Focal Loss (Object Detection)

You signed in with another tab or window. Reload to refresh your session.It is discovered that there is extreme foreground-background class imbalance problem in one-stage detector.

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And it is believed that this is the central cause which makes the performance of one-stage detectors inferior to two-stage detectors. Sik-Ho Tsang Medium. By using focal loss, the total loss can be balanced adaptively between easy samples and hard samples.

Sign in. Sik-Ho Tsang Follow. At the second stageclassification is performed for each candidate object location. Sampling heuristics using fixed foreground-to-background ratioor online hard example mining OHEM to select a small set of anchors e. Thus, there is manageable class balance between foreground and background.

One-Stage Detectors. RetinaNet Detector. Feature Pyramid Network FPN is used on top of ResNet for constructing a rich multi-scale feature pyramid from one single resolution input image. Originally, FPN is a two-stage detector which has state-of-the-art results. Please read my review about FPN if interested. FPN is multiscale, semantically strong at all scales, and fast to compute. There are some modest changes for the FPN here. A pyramid is generated from P3 to P7. Some major changes are: P2 is not used now due to computational reasons.

In total, 9 anchors per level. Across levels, scale is covered from 32 to pixels. Each anchorthere is a length K one-hot vector of classification targets K: number of classesand a 4-vector of box regression targets. Anchors are assigned to ground-truth object boxes using IoU threshold of 0.

Each anchor is assigned at most one object box, and set the corresponding class entry to one and all other entries to 0 in that K one-hot vector. If anchor is unassigned if IoU is in [0. Box regression is computed as the offset between anchor and assigned object box, or omitted if there is no assignment. It is identical to the classification subnet except that it terminates in 4 A linear outputs per spatial location. It is a class-agnostic bounding box regressor which uses fewer parameters, which is found to be equally effective.

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Inference The network only decodes box predictions from at most 1k top-scoring predictions per FPN level, after thresholding detector confidence at 0.

The top predictions from all levels are merged and non-maximum suppression NMS with a threshold of 0. COCO trainval35k split is used for training. And minival 5k split is used for validation. The vast majority of the loss comes from a small fraction of samples. FL can effectively discount the effect of easy negatives, focusing all attention on the hard negative examples. Anchor Density. Comparison with State-of-the-art Approaches 5.

Speed versus Accuracy Tradeoff. Larger backbone networks yield higher accuracy, but also slower inference speeds. Training time ranges from 10 to 35 hours. Using larger scales allows RetinaNet to surpass the accuracy of all two-stage approaches, while still being faster.In a previous blog I discussed how I built a 12 class dice detector in Tensorflow using around annotated images as training data.

The goal of the model was to detect the presence of a dice face for a 6, 8, 10, or 12 sided dice and then also determine what the face value was. Once this was done I could get the total value of the dice on the screen. This model did decently at the task, but had the issue of either not identifying dice faces or misclassifying the face values that it did detect. So at the end of that previous post I mentioned that the other approach that I would take to this problem is to build a first stage object detector that specializes in just detecting dice faces and then a second stage CNN which could use the outputs from the first model to determine the numbers.

While this adds additional complexity to the training and implementation pipelines, I felt that it could improve the overall performance.

My reasoning for the potential performance improvements are based around the advantage of using a one size fits all generalized model versus breaking down the problem into smaller pieces and building models that specialize at specific tasks.

In this case the first stage object detector can just learn to identify dice faces generically versus each type of dice individually. This means it gets more exposure to seeing dice from different angles since identifying faces on the 8 and 10 sided die pose similar issues depending on the direction. Then in the second stage CNN I can apply a large number of rotations and flips to augment the data much higher than I was able to in the generic object detection model I build previously to help it better identify dice values no matter the orientation.

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You can find the code on github here. The scripts need to be used as part of the tensorflow object detection library, and the detection scripts I modified at various points for data preparation. The ones there are the ones I used to do the final labeling of images and video as seen in the post. For this project I used the same dataset I used in my previous blog.

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I did this by quickly going through the xmls with Labelimg and adjusting the labels manually. Originally I tried to adjust the labels automatically in the generated csv from the xmls, but ran into strange model behaviors while training so I reverted to the manual method. With the single class the model only had to run for an hour or so before I stopped it at a good threshold instead of the 6 or so hours required by the previous model.

At this stage I was able to evaluate how the new object detection model was preforming in comparison to its predecessor. Something that came up quickly was that it was detecting dice that the first model missed.

two stage object detection

The image below on the left is an output from the first model whereas the right image is the same image processed by the new single class detector. The 6 on the left is detected in the new one but left out in the first model. Similar story in the following image pair. The d8 near the top was not detected by the first model, but is detected by the second.

Also both models show a weakness for 10 sided dice. In this case both models do poorly at detecting the blue 10 sided dice in the corner and in this case the first model fails more cleanly. So while the first model does a bit better at failing to identify the top of the blue 10 sided dice, I think it is also good to note that the new single class detector was trained on half as much data but does a better job of detecting dice than its predecessor overall. Now that the new dice detector was in place I could go ahead and start to build the second stage model for value classification.

The more interesting set of data preparations was getting the data for the backend CNN. I knew that I needed to train a model to identify the numbers on dice faces regardless of their orientation so I figured a CNN with heavy data augmentation in the form of random vertical and horizontal flips along with random rotations would be helpful. I decided to use a Pytorch backend model because it provides a nice simple pipeline to train and deploy its models I have also just run too many Keras models and like the variety.

Additional reasoning for using Pytorch is mostly just that I enjoy using Pytorch and have a good code base for this type of problem from my other blogs.


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