A New Perspective for Mining COCO Dataset

Athab, Suha Dh.; Nermend, Kesra; Karim, Abdulamir Abdullah

doi:10.33103/uot.ijccce.23.3.7

	A New Perspective for Mining COCO Dataset
IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING
Article 7, Volume 23, Issue 3, September 2023, Pages 80-89 PDF (810.4 K)
Document Type: Research Paper
DOI: 10.33103/uot.ijccce.23.3.7
Authors
Suha Dh. Athab^* ¹; Kesra Nermend²; Abdulamir Abdullah Karim³
¹Department of computer science university of technology
²department of computer science faculty of economic and adminisration university of Szczecin
³department of computer science university of technology baghdad iraq
Abstract
Microsoft Common Objects in Context (COCO) is a huge image dataset that has over 300 k images belonging to more than ninety-one classes. COCO has valuable information in the field of detection, segmentation, classification, and tagging; but the COCO dataset suffers from being unorganized, and classes in COCO interfere with each other. Dealing with it gives very low and unsatisfying results whether when calculating accuracy or intersection over the union in classification and segmentation algorithms. A simple method is proposed to create a customized subset from the COCO dataset by determining the class or class numbers. The suggested method is very useful as preprocessing step for any detection or segmentation algorithms such as YOLO, SSPNET, RCNN, etc. The proposed method was validated using the link net architecture for semantic segmentation. The results after applying the preprocessing were presented and compared to the state of art methods. The comparison demonstrates the exceptional effectiveness of transfer learning with our preprocessing model.
Keywords
— COCO JSON files; Object detection; object tracking; semantic segmentation

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