Small Object Detection using Deep Learning

TL;DR

This paper presents an improved Tiny YOLOv3 model for real-time detection and tracking of small drones in aerial imagery, enhancing security applications with higher accuracy and efficiency.

Contribution

The paper introduces a custom Tiny YOLOv3 model optimized for small drone detection, achieving better resource efficiency and accuracy compared to previous YOLO versions.

Findings

Recall of 93% in drone detection

Precision of 91% in drone detection

Significant improvements in resource usage and time complexity

Abstract

Now a days, UAVs such as drones are greatly used for various purposes like that of capturing and target detection from ariel imagery etc. Easy access of these small ariel vehicles to public can cause serious security threats. For instance, critical places may be monitored by spies blended in public using drones. Study in hand proposes an improved and efficient Deep Learning based autonomous system which can detect and track very small drones with great precision. The proposed system consists of a custom deep learning model Tiny YOLOv3, one of the flavors of very fast object detection model You Look Only Once (YOLO) is built and used for detection. The object detection algorithm will efficiently the detect the drones. The proposed architecture has shown significantly better performance as compared to the previous YOLO version. The improvement is observed in the terms of resource usage and time complexity. The performance is measured using the metrics of recall and precision that are 93% and 91% respectively.