Abstract
In the era of modern technology, object detection using the Gray Level Co-occurrence Matrix (GLCM) extraction method plays a crucial role in object recognition processes. It finds applications in real-time scenarios such as security surveillance and autonomous vehicle navigation, among others. Computational efficiency becomes a critical factor in achieving real-time object detection. Hence, there is a need for a detection model with low complexity and satisfactory accuracy. This research aims to enhance computational efficiency by selecting appropriate features within the GLCM framework. Two classification models, namely K-Nearest Neighbours (K-NN) and Support Vector Machine (SVM), were employed, with the results indicating that K-Nearest Neighbours (K-NN) outperforms SVM in terms of computational complexity. Specifically, K-NN, when utilizing a combination of Correlation, Energy, and Homogeneity features, achieves a 100% accuracy rate with low complexity. Moreover, when using a combination of Energy and Homogeneity features, K-NN attains an almost perfect accuracy level of 99.9889%, while maintaining low complexity. On the other hand, despite SVM achieving 100% accuracy in certain feature combinations, its high or very high complexity can pose challenges, particularly in real-time applications. Therefore, based on the trade-off between accuracy and complexity, the K-NN model with a combination of Correlation, Energy, and Homogeneity features emerges as a more suitable choice for real-time applications that demand high accuracy and low complexity. This research provides valuable insights for optimizing object detection in various applications requiring both high accuracy and rapid responsiveness.
Abstract (translated)
在现代技术时代，利用灰度级共现矩阵（GLCM）提取方法进行物体检测在物体识别过程中起着关键作用。该方法在实时场景中的应用包括安全监控和自动驾驶等。计算效率成为实现实时物体检测的关键因素。因此，在GLCM框架内选择适当的特征是提高计算效率的必要条件。 本研究旨在通过选择适当的GLCM框架内的特征来提高计算效率。采用了两种分类模型，即K-近邻（K-NN）和支持向量机（SVM）。结果表明，K-NN在计算复杂性方面优于SVM。 具体来说，当K-NN结合了相关性、能量和同质性特征时，可以达到100%的准确率，同时具有较低的复杂性。此外，当K-NN结合了能量和同质性特征时，其准确率几乎可以达到99.9889%，而保持较低的复杂性。另一方面，尽管SVM在某些特征组合上可以达到100%的准确率，但它的复杂度高或非常高，因此在实时应用程序中可能会产生挑战，特别是在实时应用程序中。因此，基于准确性和复杂性之间的平衡，结合相关性、能量和同质性特征的K-NN模型在需要高准确性和低复杂性的实时应用程序中成为更合适的选择。 这项研究为优化各种需要高准确性和快速响应的应用程序中的物体检测提供了宝贵的洞见。
URL
https://arxiv.org/abs/2404.04578
