Efficient Vehicle Detection Using Deep Learning

Abstract

In intelligent transportation systems, the suggested system uses a convolutional neural network (CNN) for object identification and classification. By using data augmentation approaches to improve model generalization and adjust to variables like weather and occlusions, it fine-tunes a pre-trained CNN model using a large annotated dataset that captures a variety of traffic events. Based on distinguishing characteristics, the CNN model is trained for multiclass classification, classifying automobiles. In an effort to increase precision and dependability in practical situations, this system provides a strong solution to the problems associated with object detection in intricate traffic situations.