SqueezeNet Based Classification for Drone vs Bird for Enhanced Recognition

Abstract

Drone growth in recent years has brought up a number of opportunities as well as challenges in a number of sectors, including aviation safety, security, and animal protection. Accurately identifying and classifying drones from birds is a significant difficulty for efficient airspace surveillance and collision avoidance systems. This paper describes a deep learning-based method that uses the SqueezeNet architecture to accurately categorize birds and drones. The labeled images in the dataset, which was divided into training, validation, and test sets, were taken from Roboflow.Comprehensive data preprocessing methods, such as augmentation and normalization, were used to improve model performance. SqueezeNet and a customized Convolutional Neural Network (CNN) were the two models created and assessed [1]. The SqueezeNet model exceeded the bespoke CNN model, which had an accuracy of 97.51%, with an amazing accuracy of 99.51%. SqueezeNet is a lightweight and efficient model whose outstanding performance highlights its applicability to real-time drone identification applications.To make sure the models were reliable and resilient, a lot of evaluation metrics and visualizations were used[2]. The study comes to the conclusion that the SqueezeNet-based classification method provides an effective way to differentiate drones from birds, boosting surveillance capabilities, safeguarding wildlife, and boosting aviation safety.This study also emphasizes how crucial careful model evaluation and data pretreatment are to getting the best results from deep learning. This technique has ramifications for many different applications, such as improving security standards, aiding in the conservation of species, and reducing aviation hazards. Prospective avenues for investigation encompass broadening the dataset, refining model architectures, executing practical deployments, and tackling moral and legal implications linked to drone detection technology. This work advances drone detection systems and highlights the ability of deep learning models to tackle challenging classification problems in a variety of settings.