Emergency Vehicle Detection Using Deep Convolutional Neural Network

TLDR: In this paper , an automated method for detecting emergency vehicles is implemented, which includes a convolutional neural network (CNN) and transfer learning technique with VGG16's fine-tuned model is employed for emergency vehicle detection.

Abstract: In densely populated cities, emergency vehicles getting caught in traffic is a regular occurrence. As a result, emergency vehicles arrive late, resulting in asset and human life losses. It is critical to treat emergency vehicles differently to avoid losses. The purpose underlying this research is to preserve human lives and reduce losses. For this, an automated method for detecting emergency vehicles is implemented. Ambulance and fire trucks are considered an emergency, and other vehicles are considered non-emergency vehicles in the proposed method. Initially, it identifies several vehicles from an image. The YOLOv4 object detector accomplished this part of the method. The identified vehicles are the region of interest for the rest of the research. Finally, the method classifies the vehicles into emergencies or non-emergencies. This study contributes by developing a model based on rigorous testing and analysis and includes a viral algorithm in deep learning: convolutional neural network (CNN). Furthermore, the transfer learning technique with VGG16’s fine-tuned model is employed for emergency vehicle detection. On the Emergency Vehicle Identification v1 dataset, this model had an average accuracy of 82.03%.