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: https://doi.org/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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