IMAV 2025 Gate Detection Dataset supports object detection for an indoor drone competition. The dataset was created by blackbeedrones for the 16th International Micro Air Vehicle Conference and Competition held in San Andres Cholula, Mexico. It focuses on detecting gates of three sizes (blue 1.5m, green 1.0m, red 0.5m) within a 10m x 10m arena.
Use Cases
- Training object detection models for gate detection based on the described three gate sizes and colors.
- Benchmarking autonomous drone navigation algorithms for indoor obstacle courses.
- Developing vision-based systems for Micro Air Vehicle (MAV) competitions based on the IMAV 2025 mission context.
Strengths
- Dataset is designed for a specific, real-world robotics competition (IMAV 2025).
- Target objects are clearly defined with three distinct sizes and colors (blue 1.5m, green 1.0m, red 0.5m).
- A pre-trained model (blackbeedrones/imav-2025-gate) is available, suggesting practical validation.
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is unknown, which may limit suitability assessment.
- Description metadata is limited; actual data quality requires manual inspection after download.
Provenance
- Source
- blackbeedrones on Hugging Face.
- Collection Method
- Created for the IMAV 2025 Indoor Competition - Mission 1.
- Time Range
- Associated with the IMAV 2025 competition.
- Freshness
- Last updated 2026-04-12 04:55:51; freshness should be verified.
- Geography
- Competition was held in San Andres Cholula, Puebla, Mexico.