VECTOR

Inspiration

The inspiration for VECTOR came from witnessing the inefficiency and danger associated with traditional cell tower inspections. Maintenance crews were regularly required to physically climb these massive structures, exposing them to significant safety risks. Additionally, as 5G networks expanded rapidly, the need for more frequent and detailed inspections became apparent. We realized that drone technology and computer vision could be combined to create a safer, faster, and more comprehensive inspection system.

What it does

VECTOR is a powerful AI-powered platform that processes drone footage to detect, classify, and analyze cell tower infrastructure. The system can: Automatically identify different tower types (lattice, monopole, guyed) Detect and count various antenna types with high precision Analyze antenna orientation and alignment issues Identify potential interference problems Perform structural analysis to detect anomalies Generate comprehensive reports with actionable insights Extract key metrics through a user-friendly interface

How we built it

We developed VECTOR using a sophisticated tech stack centered around advanced computer vision techniques: Computer Vision Core: We trained custom YOLOv8 models on a specialized dataset of cell tower imagery to accurately detect towers and their components. Video Processing Pipeline: Our system processes drone footage frame-by-frame, extracting relevant information while filtering out duplicates and noise. Web Interface: We built an intuitive Gradio-based UI that allows technicians to upload and analyze drone footage with minimal training. Advanced Analysis Module: We implemented specialized algorithms for antenna orientation analysis, interference detection, and structural analysis. Reporting System: Our platform generates detailed reports with visualizations, metrics, and recommendations.

Challenges we ran into

Building VECTOR presented several significant challenges: Model Accuracy: Cell towers vary widely in appearance and can be difficult to distinguish from other tall structures. We had to refine our models extensively to achieve reliable detection. Antenna Identification: Antennas come in many shapes and sizes, and identifying them correctly required developing specialized detection techniques. Perspective Issues: Drone footage captures towers from various angles, making consistent measurement and analysis difficult. We developed perspective-correction algorithms to address this. Video Processing Efficiency: Processing high-resolution drone footage is computationally intensive. We implemented various optimization strategies to maintain performance. Dealing with Occlusion: Weather conditions, lighting variations, and physical obstructions often hide parts of the tower. Our system had to account for partial visibility.

Accomplishments that we're proud of

What we learned

This project taught us valuable lessons about: The power of combining drone technology with AI for infrastructure inspection The challenges of training computer vision models on specialized industrial equipment Effective strategies for processing and analyzing video data at scale Designing user interfaces that present complex technical information clearly

What's next for VECTOR

We're excited to expand VECTOR's capabilities: Implementing time-series analysis to track tower conditions over multiple inspections Adding thermal imaging analysis to detect overheating components Developing 3D reconstruction of tower structures for more precise measurements Creating a mobile app for field technicians to access reports instantly Integrating with telecom inventory management systems