Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
April 15, 2026Future InternetOpen Access

Detecting Objects in Aerial Imagery Using Drones and a YOLO-C3 Hybrid Approach

View Full Paper
Ask AI
Bookmark
Share

Authors

SCSalvatore CalcagnoUniversity of CataniaAMAlessandro MidoloUniversity of CataniaESErika ScalettaUniversity of Catania

Discussion

Loading...

Member takes

Overview

Analyzing aerial imagery for object detection in natural environments, indicating enhanced accuracy and speed.

Key Points

  • The main aim is to develop an efficient hybrid system (YOLO-C3) for object detection in aerial images captured by drones.
  • Developed a hybrid YOLO-C3 approach combining advantages of neural networks and algorithmic techniques.
  • Trained on a Mediterranean imagery dataset focused on natural objects like trees and citrus groves.
  • Utilized K-fold cross-validation to evaluate the robustness of the image classifier.
  • YOLO-C3 detects a broader range of natural objects compared to existing models.
  • Achieved high accuracy with minimal latency, processing images in 0.01 seconds.
  • The hybrid system effectively adapts drone trajectories for emergency response scenarios.

Cite This Study

Calcagno et al. (2026) studied this question.

synapsesocial.com/papers/69df2b65e4eeef8a2a6b067chttps://doi.org/10.3390/fi18040204
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1LSCNet: A Lightweight Shallow Feature Cascade Network for Small Object Detection in UAV Imagery2025 · 4 citations
  2. 2DOTA: A Large-Scale Dataset for Object Detection in Aerial Images2018 · 3,304 citations
  3. 3AI-Driven Damage Detection in Wind Turbines: Drone Imagery and Lightweight Deep Learning Approaches2025 · 4 citations
  4. 4You Only Look Once: Unified, Real-Time Object Detection2016 · 51,533 citations
  5. 5Detection of Undocumented Building Constructions from Official Geodata Using a Convolutional Neural Network2020 · 16 citations