"人工智能与图像处理在未系安全带驾车检测中的应用"

人工智能和机器学习技术在未系安全带驾车检测中的应用已经取得了显著的进展。在图像处理中的种种干扰问题，如反光、纹理、遮挡等，在传统的处理方法下往往会产生一定的困难。因此，本文采用了机器学习算法来解决这些问题。通过构造不同的样本集，利用相同的算法得到两个分类器，经过大量实验验证证实了两者共同使用可以达到更好的效果。 关键词：未系安全带检测、图像处理、机器学习、反光、纹理、遮挡、分类器、实验验证、效果提升。 Abstract: The application of artificial intelligence and machine learning technologies in the detection of unfastened seat belts while driving has made significant progress. Various interferences in image processing, such as reflections, textures, and occlusions, often pose challenges with traditional methods. Therefore, this paper utilizes machine learning algorithms to address these issues. By constructing different sample sets and using the same algorithm to obtain two classifiers, it has been experimentally verified that the combined use of both can achieve better results. Keywords: unfastened seat belt detection, image processing, machine learning, reflections, textures, occlusions, classifiers, experimental verification, performance improvement.