Analysis of Deep Learning Techniques for Vehicle Detection and Reidentification Using Data from Multiple Drones and Public Datasets

Abstract

The detection and re-identification of vehicles in dynamic environments, such as highways monitored by a swarm of drones, presents significant challenges, particularly due to the variability of images captured from different angles and under various conditions. This scenario necessitates the development of suitable methods that integrate appropriate computational techniques, such as convolutional neural networks (CNN) to address the diversity of drone captures and improve accuracy in detection and re-identification. In this paper, a solution for vehicle detection and Re-ID is proposed, combining CNN techniques VGG16, VGG19, ResNet50, InceptionV3 and EfficientNetV2L. YOLOv4 was selected for detection, while the DeepSORT algorithm was chosen for tracking. The proposed solution considers the generalization capabilities of these techniques with varied images from different drones in different positions. Two datasets were employed: the first is a public dataset from Mendeley used for method evaluation, while the second consists of images and data collected by a swarm of drones. In the first experiment, the best performing network was ResNet50, with an average accuracy of 55%. In the second experiment, the highest accuracy CNN was VGG19, with 91% accuracy. Overall, the techniques were able to distinguish vehicles of different models and adapted to the data captured by drones.