- Notes: From Faster R-CNN to Mask R-CNN
- Splash of Color: Instance Segmentation with Mask R-CNN and TensorFlow
- Subscribe to RSS
- Improving the Performance of Mask R-CNN Using TensorRT
- A Brief History of CNNs in Image Segmentation: From R-CNN to Mask R-CNN
Notes: From Faster R-CNN to Mask R-CNNOur developers have a keen interest in using image recognition technologies for various purposes. Convolutional neural networks CNNs and machine learning solutions like ImageNet, Facebook facial recognition, and image captioning have already achieved a lot of progress. The main goal of these technologies is to imitate human brain activity to recognize objects in images. For instance, during work on one of our projects concerning practical implementations of convolutional neural networks, we encountered a challenge with increasing Mask R-CNN performance. Over the past few years, deep learning has continued to expand and convolutional neural networks have been released, creating a revolution in image recognition. The CNN is a class of artificial neural network that can be a powerful tool for solving various real-life tasks low traffic detection, human detection, or stationary object detection. In addition to image recognition, CNNs are constantly used for video recognition, recommendation systems, natural language processing, and other applications that involve data with a spatial structure. A CNN is an artificial neural network with a special architecture that uses relatively little pre-processing compared to other image classification algorithms. This means that the network learns the filters that in traditional algorithms were engineered by hand. A CNN is unidirectional and fundamentally multi-layered. It provides partial resistance to scale changes, offsets, turns, angle changes, and other distortions. Separation from human effort and independence in prior knowledge in future design are the central advantages of this type of network. Region-based convolutional neural networks R-CNNs and fully convolutional networks FCNs are the most recent types of convolutional neural networks. Both have been influential in semantic segmentation and object detectionhelping to solve image processing problems related to detecting sports fieldsdetecting buildingsand generating vector masks from raster data. Based on the previous version, it employs several innovations to improve training and testing speed while also increasing detection accuracy and efficiently classify object proposals using deep convolutional neural networks. Faster R-CNN uses two networks: a region proposal network for generating region proposals and a network for detecting objects. The time cost of generating region proposals is much smaller in a region proposal network than with selective search, when the region proposal network shares the most computation with the object detection network. In short, a region proposal network ranks region boxes called anchors and proposes the ones most likely to contain objects. The first stage is applied to each region of interest in order to get a binary object mask this is a segmentation process. At the first stage, a Mask R-CNN scans the image and generates proposals areas that are likely to contain objects. The second stage operates in parallel with the rest of the neural network responsible for the classification and generation of bounding boxes and masks. A binary mask is calculated for each class, and the final selection is based on the results of the classification. This type of network has shown good results in detection and segmentation as well as in detecting the posture of people. The main benefit of Mask R-CNN is that it provides the best performance among similar solutions in multiple benchmarks and can easily be adjusted for more complex tasks such as processing satellite imagery. This performance is still suitable enough for real-time tasks detecting low traffic, humans, stationary objects, etc. However, its performance may not be enough for certain cases of real-time processing or heavy image processing tasks like those related to satellite imagery. Satellite imagery is high-resolution and requires fast data collection. Moreover, WorldView-3 is able to collect data on up tosquare kilometers per day. The satellite sends a blistering 1. In this case, high-performance solutions are critical. Additionally, this five-frame-per-second performance is true only for low-resolution cameras that gather only light in the visible RGB spectrum which represent only a small part of satellite imagery. However, satellite images are made by high-resolution devices. Because of these two challenges, processing a single satellite image that comes from a modern satellite may take minutes or even hours. By using modern software-as-a-service and distributed computing frameworks, we developed an approach that allows us to boost the performance of state-of-the-art object detection solutions. However, this increase in performance is still not significant for modern quantities of data and speed of data collection. In order to further improve neural network performance, many software solutions have been developed that optimize GPU utilization. These solutions implement software capabilities to use GPU hardware and provide algorithms for distributed computing.
Splash of Color: Instance Segmentation with Mask R-CNN and TensorFlow
Mask RCNN is a deep neural network designed to address object detection and image segmentation, one of the more difficult computer vision challenges. The Mask RCNN model generates bounding boxes and segmentation masks for each instance of an object in the image. This tutorial uses tf. TPUEstimator to train the model. Use the pricing calculator to generate a cost estimate based on your projected usage. New Google Cloud users might be eligible for a free trial. Before you begin Before starting this tutorial, check that your Google Cloud project is correctly set up. If you don't already have one, sign up for a new account. Go to the project selector page. Make sure that billing is enabled for your Google Cloud project. Learn how to confirm billing is enabled for your project. This walkthrough uses billable components of Google Cloud. Check the Cloud TPU pricing page to estimate your costs. Be sure to clean up resources you create when you've finished with them to avoid unnecessary charges. Open Cloud Shell. Configure gcloud command-line tool to use the project where you want to create the Cloud TPU. This Cloud Storage bucket stores the data you use to train your model and the training results. The ctpu up tool used in this tutorial sets up default permissions for the Cloud TPU service account. If you want finer-grain permissions, review the access level permissions. VMs and TPU nodes are located in specific zoneswhich are subdivisions within a region. The configuration you specified appears. Enter y to approve or n to cancel. When the ctpu up command has finished executing, verify that your shell prompt has changed from username projectname to username vm-name. This change shows that you are now logged into your Compute Engine VM. This tutorial requires a long-lived connection to the Compute Engine instance. To ensure you aren't disconnected from the instance, run the following command:. Add an environment variable for your storage bucket.
Subscribe to RSS
And, second, how to train a model from scratch and use it to build a smart color splash filter. Including the dataset I built and the trained model. Follow along! Instance segmentation is the task of identifying object outlines at the pixel level. Consider the following asks:. Mask R-CNN regional convolutional neural network is a two stage framework: the first stage scans the image and generates proposals areas likely to contain an object. And the second stage classifies the proposals and generates bounding boxes and masks. This is a standard convolutional neural network typically, ResNet50 or ResNet that serves as a feature extractor. The early layers detect low level features edges and cornersand later layers successively detect higher level features car, person, sky. Passing through the backbone network, the image is converted from xpx x 3 RGB to a feature map of shape 32x32x This feature map becomes the input for the following stages. The code supports ResNet50 and ResNet While the backbone described above works great, it can be improved upon. FPN improves the standard feature extraction pyramid by adding a second pyramid that takes the high level features from the first pyramid and passes them down to lower layers. By doing so, it allows features at every level to have access to both, lower and higher level features. The section after building the ResNet. RPN introduces additional complexity: rather than a single backbone feature map in the standard backbone i. We pick which to use dynamically depending on the size of the object. The RPN is a lightweight neural network that scans the image in a sliding-window fashion and finds areas that contain objects. The regions that the RPN scans over are called anchors. Which are boxes distributed over the image area, as show on the left. This is a simplified view, though. In practice, there are about K anchors of different sizes and aspect ratios, and they overlap to cover as much of the image as possible. How fast can the RPN scan that many anchors? Pretty fast, actually. The sliding window is handled by the convolutional nature of the RPN, which allows it to scan all regions in parallel on a GPU. Instead, the RPN scans over the backbone feature map. This allows the RPN to reuse the extracted features efficiently and avoid duplicate calculations. The RPN generates two outputs for each anchor:.
Improving the Performance of Mask R-CNN Using TensorRT
Last Updated on October 3, Object detection is a task in computer vision that involves identifying the presence, location, and type of one or more objects in a given photograph. It is a challenging problem that involves building upon methods for object recognition e. In recent years, deep learning techniques have achieved state-of-the-art results for object detection, such as on standard benchmark datasets and in computer vision competitions. In this tutorial, you will discover how to use the Mask R-CNN model to detect objects in new photographs. Discover how to build models for photo classification, object detection, face recognition, and more in my new computer vision bookwith 30 step-by-step tutorials and full source code. Object detection is a computer vision task that involves both localizing one or more objects within an image and classifying each object in the image. It is a challenging computer vision task that requires both successful object localization in order to locate and draw a bounding box around each object in an image, and object classification to predict the correct class of object that was localized. An extension of object detection involves marking the specific pixels in the image that belong to each detected object instead of using coarse bounding boxes during object localization. This harder version of the problem is generally referred to as object segmentation or semantic segmentation. There are perhaps four main variations of the approach, resulting in the current pinnacle called Mask R-CNN. The salient aspects of each variation can be summarized as follows:. The paper provides a nice summary of the model linage to that point:. The Region-based CNN R-CNN approach to bounding-box object detection is to attend to a manageable number of candidate object regions and evaluate convolutional networks independently on each RoI. Faster R-CNN is flexible and robust to many follow-up improvements, and is the current leading framework in several benchmarks. The family of methods may be among the most effective for object detection, achieving then state-of-the-art results on computer vision benchmark datasets. Although accurate, the models can be slow when making a prediction as compared to alternate models such as YOLO that may be less accurate but are designed for real-time prediction. Mask R-CNN is a sophisticated model to implement, especially as compared to a simple or even state-of-the-art deep convolutional neural network model. Source code is available for each version of the R-CNN model, provided in separate GitHub repositories with prototype models based on the Caffe deep learning framework. For example:. Instead of developing an implementation of the R-CNN or Mask R-CNN model from scratch, we can use a reliable third-party implementation built on top of the Keras deep learning framework. The project is open source released under a permissive license i. MIT license and the code has been widely used on a variety of projects and Kaggle competitions. Nevertheless, it is an open source project, subject to the whims of the project developers. As such, I have a fork of the project availablejust in case there are major changes to the API in the future. The project is light on API documentation, although it does provide a number of examples in the form of Python Notebooks that you can use to understand how to use the library by example. Two notebooks that may be helpful to review are:. In order to get familiar with the model and the library, we will look at the first example in the next section. Much like using a pre-trained deep CNN for image classification, e. At the time of writing, there is no distributed version of the library, so we have to install it manually. The good news is that this is very easy. Installation involves cloning the GitHub repository and running the installation script on your workstation. On Linux or MacOS you may need to install the software with sudo permissions; for example, you may see an error such as:. The library will then install directly and you will see a lot of successful installation messages ending with the following:. This confirms that you installed the library successfully and that you have the latest version, which at the time of writing is version 2. You can confirm that the library was installed correctly by querying it via the pip command; for example:. The weights are available from the project GitHub project and the file is about megabytes. We will use a photograph from Flickr released under a permissive license, specifically a photograph of an elephant taken by Mandy Goldberg.