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

• URL: http://arxiv.org/abs/2405.05551v1
• Date: Thu, 9 May 2024 05:21:42 GMT
• Title: The object detection model uses combined extraction with KNN and RF classification
• Authors: Florentina Tatrin Kurniati, Daniel HF Manongga, Irwan Sembiring, Sutarto Wijono, Roy Rudolf Huizen,
• Abstract summary: 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. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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

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