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

翻译：目标检测在众多领域发挥着重要作用。开发能处理旋转与纹理变化的二维目标检测模型仍是一项挑战。本研究提出的模型初始阶段整合了灰度共生矩阵（GLCM）与局部二值模式（LBP）纹理特征提取方法以获取特征向量。后续阶段采用K近邻（KNN）、随机森林（RF）及投票集成（VE）进行特征分类。系统测试使用包含4,437张二维图像的数据集，结果表明KNN准确率为92.7%、F1分数为92.5%，而RF性能较低。虽然GLCM特征在两个算法上均能提升性能，但KNN表现更为稳定。VE方法以93.9%的准确率和93.8%的F1分数展现出最佳性能，验证了集成技术对提升目标检测准确率的有效性。本研究通过将GLCM与LBP结合作为特征向量，并采用VE分类方法，为目标检测领域提供了新思路。