How to Use Mask R-CNN in Keras for Object Detection in Photographs

Last Updated on September 2, 2020

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.g. where are they), object localization (e.g. what are their extent), and object classification (e.g. what are they).

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. Most notably is the R-CNN, or Region-Based Convolutional Neural Networks, and the most recent technique called Mask R-CNN that is capable of achieving state-of-the-art results on a range of object detection tasks.

In this tutorial, you will discover how to use the Mask R-CNN model to detect objects in new photographs.

After completing this tutorial, you will know:

• The region-based Convolutional Neural Network family of models for object detection and the most recent variation called Mask R-CNN.
• The best-of-breed open source library implementation of the Mask R-CNN for the Keras deep learning library.
• How to use a pre-trained Mask R-CNN to perform object localization and detection on new photographs.

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