Improving Small Drone Detection Through Multi-Scale Processing and Data Augmentation

• URL: http://arxiv.org/abs/2504.19347v1
• Date: Sun, 27 Apr 2025 20:06:55 GMT
• Title: Improving Small Drone Detection Through Multi-Scale Processing and Data Augmentation
• Authors: Rayson Laroca, Marcelo dos Santos, David Menotti,
• Abstract summary: This work introduces a drone detection methodology built upon the medium-sized YOLOv11 object detection model.<n>To enhance its performance on small targets, we implemented a multi-scale approach in which the input image is processed both as a whole and in segmented parts, with subsequent prediction aggregation.<n>The proposed approach attained a top-3 ranking in the 8th WOSDETC Drone-vsBird Detection Grand Challenge, held at the 2025 International Joint Conference on Neural Networks.
• Score: 2.522137108227868
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Detecting small drones, often indistinguishable from birds, is crucial for modern surveillance. This work introduces a drone detection methodology built upon the medium-sized YOLOv11 object detection model. To enhance its performance on small targets, we implemented a multi-scale approach in which the input image is processed both as a whole and in segmented parts, with subsequent prediction aggregation. We also utilized a copy-paste data augmentation technique to enrich the training dataset with diverse drone and bird examples. Finally, we implemented a post-processing technique that leverages frame-to-frame consistency to mitigate missed detections. The proposed approach attained a top-3 ranking in the 8th WOSDETC Drone-vsBird Detection Grand Challenge, held at the 2025 International Joint Conference on Neural Networks (IJCNN), showcasing its capability to detect drones in complex environments effectively.

Related papers

• YOLOMG: Vision-based Drone-to-Drone Detection with Appearance and Pixel-Level Motion Fusion [9.810747004677474]
  This paper proposes a novel end-to-end framework that accurately identifies small drones in complex environments.<n>It starts by creating a motion difference map to capture the motion characteristics of tiny drones.<n>Next, this motion difference map is combined with an RGB image using a bimodal fusion module, allowing for adaptive feature learning of the drone.
  arXiv  Detail & Related papers  (2025-03-10T09:44:21Z)
• A Cross-Scene Benchmark for Open-World Drone Active Tracking [54.235808061746525]
  Drone Visual Active Tracking aims to autonomously follow a target object by controlling the motion system based on visual observations.<n>We propose a unified cross-scene cross-domain benchmark for open-world drone active tracking called DAT.<n>We also propose a reinforcement learning-based drone tracking method called R-VAT.
  arXiv  Detail & Related papers  (2024-12-01T09:37:46Z)
• DroBoost: An Intelligent Score and Model Boosting Method for Drone Detection [1.2564343689544843]
  Drone detection is a challenging object detection task where visibility conditions and quality of the images may be unfavorable. Our work improves on the previous approach by combining several improvements. The proposed technique won 1st Place in the Drone vs. Bird Challenge.
  arXiv  Detail & Related papers  (2024-06-30T20:49:56Z)
• C2FDrone: Coarse-to-Fine Drone-to-Drone Detection using Vision Transformer Networks [23.133250476580038]
  A vision-based drone-to-drone detection system is crucial for various applications like collision avoidance, countering hostile drones, and search-and-rescue operations. detecting drones presents unique challenges, including small object sizes, distortion, and real-time processing requirements. We propose a novel coarse-to-fine detection strategy based on vision transformers.
  arXiv  Detail & Related papers  (2024-04-30T05:51:21Z)
• TransVisDrone: Spatio-Temporal Transformer for Vision-based Drone-to-Drone Detection in Aerial Videos [57.92385818430939]
  Drone-to-drone detection using visual feed has crucial applications, such as detecting drone collisions, detecting drone attacks, or coordinating flight with other drones. Existing methods are computationally costly, follow non-end-to-end optimization, and have complex multi-stage pipelines, making them less suitable for real-time deployment on edge devices. We propose a simple yet effective framework, itTransVisDrone, that provides an end-to-end solution with higher computational efficiency.
  arXiv  Detail & Related papers  (2022-10-16T03:05:13Z)
• Sequence Models for Drone vs Bird Classification [2.294014185517203]
  Drone detection has become an essential task in object detection as drone costs have decreased and drone technology has improved. It is difficult to detect distant drones when there is weak contrast, long range, and low visibility. We propose several sequence classification architectures to reduce the detected false-positive ratio of drone tracks.
  arXiv  Detail & Related papers  (2022-07-21T11:00:44Z)
• Track Boosting and Synthetic Data Aided Drone Detection [0.0]
  Our method approaches the drone detection problem by fine-tuning a YOLOv5 model with real and synthetically generated data. Our results indicate that augmenting the real data with an optimal subset of synthetic data can increase the performance.
  arXiv  Detail & Related papers  (2021-11-24T10:16:27Z)
• Rethinking Drone-Based Search and Rescue with Aerial Person Detection [79.76669658740902]
  The visual inspection of aerial drone footage is an integral part of land search and rescue (SAR) operations today. We propose a novel deep learning algorithm to automate this aerial person detection (APD) task. We present the novel Aerial Inspection RetinaNet (AIR) algorithm as the combination of these contributions.
  arXiv  Detail & Related papers  (2021-11-17T21:48:31Z)
• Dogfight: Detecting Drones from Drones Videos [58.158988162743825]
  This paper attempts to address the problem of drones detection from other flying drones variations. The erratic movement of the source and target drones, small size, arbitrary shape, large intensity, and occlusion make this problem quite challenging. To handle this, instead of using region-proposal based methods, we propose to use a two-stage segmentation-based approach.
  arXiv  Detail & Related papers  (2021-03-31T17:43:31Z)
• Perceiving Traffic from Aerial Images [86.994032967469]
  We propose an object detection method called Butterfly Detector that is tailored to detect objects in aerial images. We evaluate our Butterfly Detector on two publicly available UAV datasets (UAVDT and VisDrone 2019) and show that it outperforms previous state-of-the-art methods while remaining real-time.
  arXiv  Detail & Related papers  (2020-09-16T11:37:43Z)
• Detection and Tracking Meet Drones Challenge [131.31749447313197]
  This paper presents a review of object detection and tracking datasets and benchmarks, and discusses the challenges of collecting large-scale drone-based object detection and tracking datasets with manual annotations. We describe our VisDrone dataset, which is captured over various urban/suburban areas of 14 different cities across China from North to South. We provide a detailed analysis of the current state of the field of large-scale object detection and tracking on drones, and conclude the challenge as well as propose future directions.
  arXiv  Detail & Related papers  (2020-01-16T00:11:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.