Once Learning for Looking and Identifying Based on YOLO-v5 Object Detection
Analysis of the ability of a machine learning framework named “You Only Look Once,” to perform object localization task in a “Heuristic once learning” context showed that YOLO had difficulties to generalize simple abstractions of the characters, pointing to the necessity of new approaches to solve such challenges.
Abstract
Object detection is an essential capacity of computer vision solutions. It has gained attention over the last years by using a core component of the “Once learning” and “Few-shot learning” mechanism. This research analyzes the ability of a machine learning framework named “You Only Look Once,” to perform object localization task in a “Heuristic once learning” context. It will also study the advantages and practical limitations of YOLO by experimenting with two types of implementation: 1) the simplest one (a.k.a tiny YOLO), and 2) the first version of YOLO. The case studies are carried out in various visual data types and object contexts, such as object deformation caused by fast-forward frame, spatial distortion caused by isometric projection, and gaming images with abnormal objects. Finally, we build a dataset accounting for a new task so-called “Heuristic once learning”. Results using YOLO-v5 in such conditions showed that YOLO had difficulties to generalize simple abstractions of the characters, pointing to the necessity of new approaches to solve such challenges.