Abstract
Video segmentation is a type of deep learning algorithm that enables autonomous vehicles to perceive and interpret real-world scenes in real-time [19]. Since video footage comprises multiple static frames, a fast image segmentation algorithm can be utilized for video segmentation. Image segmentation is divided into two categories: instance segmentation and semantic segmentation. Instance segmentation is superior to semantic segmentation because it preserves the 3D spatial location of objects. Furthermore, an instance segmentation algorithm is an object detection algorithm that can generate a pixel-wise mask for each object instance [34]. This paper discusses the fundamental components of two model families, R-CNN and YOLO, as well as the evaluation metrics and a comparison between a version of the R-CNN (Mask R-CNN) model and a version of the YOLO (YOLOv5) model.
Advisor
Kelvey, Rob
Second Advisor
Taylor, Max
Department
Computer Science; Mathematics
Recommended Citation
Dao, Minh Duc, "An Exploration Into Image Object Detection and Image Instance Segmentation: Mask R-CNN and YOLOv5 Comparison in Image Object Detection Task" (2023). Senior Independent Study Theses. Paper 10459.
https://openworks.wooster.edu/independentstudy/10459
Disciplines
Robotics
Publication Date
2023
Degree Granted
Bachelor of Arts
Document Type
Senior Independent Study Thesis
External Link
https://github.com/ducdao-git/seniorIS_code
© Copyright 2023 Minh Duc Dao