Abstract—The YOLO tool has been increasingly developed to assist in object classification. However, the problem of object classification has many difficulties, including small objects, background effects, or noise loss of information. Therefore, to evaluate the objective of classifying small, information-losing objects, the research team installed and evaluated new YOLO models such as YOLOv5, YOLOv6, and YOLOv7. In addition, the study conducted a feature-based object classification test for comparison and evaluation. The study was conducted on the self-collected data set, divided into 2 datasets: a dataset used to evaluate object classification and a dataset used to classify by features. The evaluation results show certain advantages of the YOLOv7 model on parameters such as Precision, Recall, and a mAP threshold of 50%. The evaluation results show certain advantages of the YOLOv7 model on parameters such as Precision, Recall, and a mAP threshold of 50%. The study results show that YOLOv7 achieves specific effects when the accuracy of object recognition is over 90%, in which the feature-based classification also achieves an accuracy of over 70%. This issue may need different future studies in object recognition and object feature recognition.
 
Keywords—YOLOv5, YOLOv6, YOLOv7, small object, rice seed, object detection, feature classification


Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.