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

arXiv:2405.05551v1 Announce Type: new
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 …

abstract arxiv binary challenge classification cs.cv detection experience extraction fields knn matrix object objects research role rotation stage texture type