The object detection model uses combined extraction with KNN and RF classification

TL;DR

This paper presents a new object detection approach combining GLCM and LBP feature extraction with ensemble classification using KNN, RF, and voting to improve detection accuracy on 2D images with rotation and texture variations.

Contribution

It introduces a novel combination of texture features and ensemble classification techniques for enhanced 2D object detection.

Findings

VE achieves highest accuracy of 93.9%

KNN outperforms RF in accuracy and consistency

Ensemble voting improves overall detection performance

Abstract

Object detection plays an important role in various fields. Developing detection models for 2D objects that experience rotation and texture variations is a challenge. In this research, the initial stage of the proposed model integrates the gray-level co-occurrence matrix (GLCM) and local binary patterns (LBP) texture feature extraction to obtain feature vectors. The next stage is classifying features using k-nearest neighbors (KNN) and random forest (RF), as well as voting ensemble (VE). System testing used a dataset of 4,437 2D images, the results for KNN accuracy were 92.7% and F1-score 92.5%, while RF performance was lower. Although GLCM features improve performance on both algorithms, KNN is more consistent. The VE approach provides the best performance with an accuracy of 93.9% and an F1 score of 93.8%, this shows the effectiveness of the ensemble technique in increasing object detection accuracy. This study contributes to the field of object detection with a new approach combining GLCM and LBP as feature vectors as well as VE for classification