exploring object detection methods for autonomous vehicles perception: a comparative study of classical and deep learning approaches

This paper explores the performance of various object detection techniques for autonomous vehicle perception by analyzing classical machine learning and recent deep learning models. we evaluate three classical methods, including pca, hog, and hog alongside different versions of the svm classifier, and five deep-learning models, including faster-rcnn, ssd, yolov3, yolov5, and yolov9 models using the benchmark inria dataset. the experimental results show that although classical methods such as hog + gaussian svm outperform other classical approaches, they are outperformed by deep learning techniques. furthermore, classical methods have limitations in detecting partially occluded, distant objects and complex clothing challenges, while recent deep-learning models are more efficient and provide better performance (yolov9) on these challenges.