Nicole Adelson - Student Research and Creativity Forum - Hofstra University

Using Mask R-CNN Modeling for Object Detection and Its Various Applications

By: Nicole Adelson

Mentors: Dr. Edward Currie and Dr. Yimin Zhao

Objectives

● To explore Mask Region-based Convolutional Neural Network (Mask R-CNN) to develop a model that uses pixel-level segmentation to aid in detecting specific objects in an image

● To identify applications that can use the method of object detection in a variety of fields

Main Python Libraries

● Tensorflow

● Keras

Chosen IDE

● PyCharm

Supporting Python Libraries Including, but not limited to:

● NumPy

● Skit-Image

● Matplotlib

Step 1: Establishing the Dataset

Step 2: Parsing the Data

Output:

Dimensions of Each Bounding Box

Step 3: Developing the Object

Output:

Step 4: Testing Object - Algorithm

Step 4: Testing Object - Output

In another tab: In console:

Dimensions of the displayed bounding boxes

Step 6: Training the Mask R-CNN Model for the Dataset - Preparation

Necessary file for weights:

Step

7: Training the Mask R-CNN Model for the Dataset - Algorithm

Step 7: Training the Mask R-CNN Model for the Dataset - Output

Step

8: Evaluating the Mask-RCNN ModelAlgorithm

Step 8: Evaluating the Mask-RCNN Model - Output

→ 90.6% accuracy

→ 94.3% accuracy

Step 10: Detecting Objects in New PhotosAlgorithm Pt.1

Step 10: Detecting Objects in New Photos -

Algorithm Pt.2

Step 10: Detecting Objects in New PhotosOutput

Applications for the Mask R-CNN Model

● Speech recognition

● Text recognition

● Fraud detection

● Stock market projections

● Medical symptom identification

● And much more!

Source for Model Algorithm Inspiration

Brownlee, Jason. “How to Train an Object Detection Model with Keras.”

MachineLearningMastery.Com, 1 Sept. 2020, machinelearningmastery.com/how-to-train-an-object-detection-model-with-keras/.