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

2025-12-19

AI Summary

To address the challenges of real-time small-object detection on micro UAVs—namely, tiny object size, high motion velocity, and low contrast—this paper proposes YOLOvN-CBi, a lightweight and efficient architecture. We introduce the novel CBi module, which synergistically integrates CBAM (Convolutional Block Attention Module) and BiFPN (Bidirectional Feature Pyramid Network), and design four CBi variants. Furthermore, knowledge distillation is employed, transferring knowledge from a teacher model (YOLOv5m-CBi) to a student model (YOLOv5n-CBi) for edge deployment. Evaluated on a custom dataset comprising 28K training images and a dedicated test set of 2,500 ultra-small targets, the distilled model achieves an mAP@0.5:0.9 of 0.6573 (+6.51% improvement over baseline), with 82.9% faster inference speed. It significantly outperforms YOLOv8 and YOLOv12, establishing state-of-the-art trade-offs between accuracy and speed for small-object detection.

Technology Category

• Computer Vision: Object Detection & Categorization
• Machine Learning: Learning on the Edge & Model Compression
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• Detects small drones in real-time for security.
• Improves accuracy and speed for small object detection.
• Enables efficient edge deployment with lightweight models.

Innovation

• Modified YolovN with CBAM and BiFPN for small object sensitivity
• Knowledge distillation for faster, lighter edge deployment
• Outperforms newer Yolo versions in speed-accuracy trade-off