YolovN-CBi: A Lightweight and Efficient Architecture for Real-Time Detection of Small UAVs

TL;DR

This paper introduces YolovN-CBi, a lightweight, efficient architecture enhanced with attention modules and feature pyramids, achieving superior real-time small UAV detection performance and speed, validated on multiple datasets.

Contribution

The paper proposes YolovN-CBi, a novel architecture combining CBAM and BiFPN, with variants and knowledge distillation, improving small UAV detection accuracy and speed for real-time applications.

Findings

YolovN-CBi outperforms Yolov5, Yolov8, and Yolov12 in speed-accuracy trade-offs.

Distilled models are significantly faster and more accurate, suitable for edge deployment.

The architecture effectively detects small UAVs in diverse datasets.

Abstract

Unmanned Aerial Vehicles, commonly known as, drones pose increasing risks in civilian and defense settings, demanding accurate and real-time drone detection systems. However, detecting drones is challenging because of their small size, rapid movement, and low visual contrast. A modified architecture of YolovN called the YolovN-CBi is proposed that incorporates the Convolutional Block Attention Module (CBAM) and the Bidirectional Feature Pyramid Network (BiFPN) to improve sensitivity to small object detections. A curated training dataset consisting of 28K images is created with various flying objects and a local test dataset is collected with 2500 images consisting of very small drone objects. The proposed architecture is evaluated on four benchmark datasets, along with the local test dataset. The baseline Yolov5 and the proposed Yolov5-CBi architecture outperform newer Yolo versions, including Yolov8 and Yolov12, in the speed-accuracy trade-off for small object detection. Four other variants of the proposed CBi architecture are also proposed and evaluated, which vary in the placement and usage of CBAM and BiFPN. These variants are further distilled using knowledge distillation techniques for edge deployment, using a Yolov5m-CBi teacher and a Yolov5n-CBi student. The distilled model achieved a mA@P0.5:0.9 of 0.6573, representing a 6.51% improvement over the teacher's score of 0.6171, highlighting the effectiveness of the distillation process. The distilled model is 82.9% faster than the baseline model, making it more suitable for real-time drone detection. These findings highlight the effectiveness of the proposed CBi architecture, together with the distilled lightweight models in advancing efficient and accurate real-time detection of small UAVs.