An Improved Moving Object Detection in a Wide Area Environment using Image Classification and Recognition by Comparing You Only Look Once (YOLO) Algorithm over Deformable Part Models (DPM) Algorithm.
DOI:
https://doi.org/10.47750/pnr.2022.13.S04.204Keywords:
Object Identification, Novel Image Classification, Deformable Part Model, Bounding Boxes, Non-Maximal Suppression (NMS), Convolutional Neural Network, You Only Look Once.Abstract
Aim: The objective of the research is to increase the precision of object detection using novel image classification using machine learning algorithms. Materials and Methods: The categorising is performed by adopting a sample size of n = 10 in You Only Look Once (YOLO) and sample size n = 10 in Deformable Part Model (DPM) algorithms with a sample size = 10 and the G-Power analysis was carried out with 80% and confidence interval 95%. Results and Discussion: The experiment outcomes shows that the You Only Look Once (YOLO) has a high accuracy of 90.78% in comparison with the 83.47%. A statistically significant difference exists between the research groups with p=0.001 (2 tailed) (p<0.05). Conclusion: Detection of items and entities with high accuracy using machine learning algorithms shows that the You Only Look Once (YOLO) generates higher accuracy than the Deformable Part Model (DPM) algorithm.
