Skip to main content

Coding Your Future: A Guidebook for Students

18 Opportunities with AD Aerospace

Our friends at AD Aeropace (see figure 18.1)in Stockport are looking for an AI App Developer to work as a summer intern on a Visual Darts Game, to develop an AI-Based Throwing Technique Analysis App.

  • 📌 Project Title DartVision: AI-Powered Darts Throw Analyzer
  • 🎯 Objective: Develop an AI-based application that captures high-resolution video (4K) of a darts player using a 4K AI enabled camera (client provided), analyzes the throwing technique in real-time or post-processing, and outputs annotated video feedback to help the player improve their form and accuracy.
AD Aerospace is part of the Mythra Group of companies, pioneers and market leaders in state-of-the-art CCTV solutions. Screenshot of company website ad-aero.com

Figure 18.1: AD Aerospace is part of the Mythra Group of companies, pioneers and market leaders in state-of-the-art CCTV solutions. Screenshot of company website ad-aero.com

18.1 Core Features & Deliverables

The core features required of the app are:

18.1.1 Video Input

  • Support for 4K camera input (e.g., webcam or phone camera).
  • Record short video clips (5–10 seconds) of a player throwing darts.

18.1.2 Pose Detection & Tracking

  • Use computer vision (e.g., OpenPose, Mediapipe) to track the player’s body and arm movement and dart accuracy.
  • Key points: shoulder, elbow, wrist, fingers.

18.1.3 Trajectory Estimation

  • Optional: Track dart trajectory on camera.
  • Estimate release point, angle, speed, board entry point.

18.1.4 Technique Analysis

  • Detect throwing technique metrics such as:
    • Arm extension angle
    • Wrist flick timing
    • Body stability/posture
  • Compare against a template (e.g., professional throw) or user’s past performance

18.1.5 Feedback Output

  • Output annotated video highlighting/troubleshooting:
    • Detected joint movement
    • Deviations from optimal form
  • Provide textual or audio suggestions

18.1.6 User Interface

  • Simple UI to record, replay, and analyze throws
  • Option to save or export the analysed video
  • Basic dashboard showing performance trends over time

18.1.7 Technical Stack Suggestions

  • Language: Python (preferred for prototyping), or JavaScript (if browser-based).
  • Libraries/Tools:
    • OpenCV for video processing.
    • MediaPipe / OpenPose for pose estimation.
    • TensorFlow or PyTorch (optional for custom ML models).
    • Streamlit, PyQt, or Electron for UI.
  • Hardware: Compatible with 4K cameras (AI enabled camera will be provided).

18.2 Timeline

The timeline for the project is as follows:

  • Week 1: Research and prototype pose tracking on darts videos
  • Week 2-3: Build video input + processing pipeline
  • Week 4-5: Implement technique analysis algorithms
  • Week 6-7: Add annotated video output and feedback generation
  • Week 6-7: UI development and integration
  • Week 8: User testing and performance optimization
  • Week 8: Final demo and documentation

18.3 Interested?

Location, Stockport, close to public transport links. Salary – £10-12/hr dependant on experience, send a cover letter and debugged CV to talent@w3associates.co.uk by 5pm on 27th June.