Data Science and Intelligent Computing Techniques
Mask R-CNN: A Comparative Study On Improvements In Object Detection And Segmentation
Authors: Shashwat Shukla, Rahul Kumar Verma, Narayana Darapaneni, Anwesh Reddy Paduri, Sudha B G, Abhishek K, Arun Kumar BS, Chandrashekar KP, Deepak Kumar T P and Prajwal S Shetty
Publishing Date: 12-01-2023
ISBN: 978-81-955020-2-8
Abstract
Abstract. Over the years there has been an increasing demand for image recognition as the world is moving towards a digital space. With the increasing demands, the application of Mask RCNN and expanded algorithms based on segmentation and YOLO have seen a major rise in the last 5 years. So the accuracy of the models has improved slightly since most projects take different approaches to the different datasets and have different metrics of Evaluation. By cross-referencing these approaches, a trend is observed that leads to higher success with the implementation of models using the hyperparameters and extra layers that have been added to the Mask RCNN in sequences. In this paper, the different approaches taken by different researchers are explored to understand how the implementations have progressed over the last half of the decade. In our research, we have studied & analyzed 50 papers and found that the majority of papers were using the COCO dataset for training purposes with a specified set of hyper-parameters to measure the accuracy, performance, and memory consumption. The experiment findings were presented to suggest suitable RCNN architecture based on application or hardware attributes.
Keywords
Object Detection, Segmentation, Fast RCNN, Faster RCNN, Mask R-CNN
Cite as
Shashwat Shukla, Rahul Kumar Verma, Narayana Darapaneni, Anwesh Reddy Paduri, Sudha B G, Abhishek K, Arun Kumar BS, Chandrashekar KP, Deepak Kumar T P and Prajwal S Shetty, "Mask R-CNN: A Comparative Study On Improvements In Object Detection And Segmentation", In: Satyasai Jagannath Nanda and Rajendra Prasad Yadav (eds), Data Science and Intelligent Computing Techniques, SCRS, India, 2023, pp. 93-102. https://doi.org/10.56155/978-81-955020-2-8-9