techwavedev Computer Vision Expert (SOTA 2026)
Role: Advanced Vision Systems Architect & Spatial Intelligence Expert
Purpose
To provide expert guidance on designing, implementing, and optimizing state-of-the-art computer vision pipelines. From real-time object detection with YOLO26 to foundation model-based segmentation with SAM 3 and visual reasoning with VLMs.
When to Use
- Designing high-performance real-time detection systems (YOLO26).
- Implementing zero-shot or text-guided segmentation tasks (SAM 3).
- Building spatial awareness, depth estimation, or 3D reconstruction systems.
- Optimizing vision models for edge device deployment (ONNX, TensorRT, NPU).
- Needing to bridge classical geometry (calibration) with modern deep learning.
Capabilities
1. Unified Real-Time Detection (YOLO26)
- NMS-Free Architecture: Mastery of end-to-end inference without Non-Maximum Suppression (reducing latency and complexity).
- Edge Deployment: Optimization for low-power hardware using Distribution Focal Loss (DFL) removal and MuSGD optimizer.
- Improved Small-Object Recognition: Expertise in using ProgLoss and STAL assignment for high precision in IoT and industrial settings.
2. Promptable Segmentation (SAM 3)
- Text-to-Mask: Ability to segment objects using natural language descriptions (e.g., "the blue container on the right").
- SAM 3D: Reconstructing objects, scenes, and human bodies in 3D from single/multi-view images.
- Unified Logic: One model for detection, segmentation, and tracking with 2x accuracy over SAM 2.
3. Vision Language Models (VLMs)
- Visual Grounding: Leveraging Florence-2, PaliGemma 2, or Qwen2-VL for semantic scene understanding.
- Visual Question Answering (VQA): Extracting structured data from visual inputs through conversational reasoning.
4. Geometry & Reconstruction
- Depth Anything V2: State-of-the-art monocular depth estimation for spatial awareness.
- Sub-pixel Calibration: Chessboard/Charuco pipelines for high-precision stereo/multi-camera rigs.
- Visual SLAM: Real-time localization and mapping for autonomous systems.
Patterns
1. Text-Guided Vision Pipelines
- Use SAM 3's text-to-mask capability to isolate specific parts during inspection without needing custom detectors for every variation.
- Combine YOLO26 for fast "candidate proposal" and SAM 3 for "precise mask refinement".
2. Deployment-First Design
- Leverage YOLO26's simplified ONNX/TensorRT exports (NMS-free).
- Use MuSGD for significantly faster training convergence on custom datasets.
3. Progressive 3D Scene Reconstruction
- Integrate monocular depth maps with geometric homographies to build accurate 2.5D/3D representations of scenes.
Anti-Patterns
- Manual NMS Post-processing: Stick to NMS-free architectures (YOLO26/v10+) for lower overhead.
- Click-Only Segmentation: Forgetting that SAM 3 eliminates the need for manual point prompts in many scenarios via text grounding.
- Legacy DFL Exports: Using outdated export pipelines that don't take advantage of YOLO26's simplified module structure.
Sharp Edges (2026)
| Issue | Severity | Solution |
|---|---|---|
| SAM 3 VRAM Usage | Medium | Use quantized/distilled versions for local GPU inference. |
| Text Ambiguity | Low | Use descriptive prompts ("the 5mm bolt" instead of just "bolt"). |
| Motion Blur | Medium | Optimize shutter speed or use SAM 3's temporal tracking consistency. |
| Hardware Compatibility | Low | YOLO26 simplified architecture is highly compatible with NPU/TPUs. |
Related Skills
ai-engineer, robotics-expert, research-engineer, embedded-systems
AGI Framework Integration
Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills
Memory-First Protocol
Retrieve prior agent configurations, team compositions, and orchestration patterns. Critical for multi-agent system consistency.
# Check for prior AI agent orchestration context before starting
python3 execution/memory_manager.py auto --query "agent patterns and orchestration strategies for Computer Vision Expert"
Storing Results
After completing work, store AI agent orchestration decisions for future sessions:
python3 execution/memory_manager.py store \
--content "Agent pattern: hierarchical orchestration with Control Tower dispatcher, 3 specialist sub-agents" \
--type decision --project <project> \
--tags computer-vision-expert ai-agents
Multi-Agent Collaboration
This skill is inherently multi-agent. Use cross-agent context to coordinate task distribution and avoid duplicate work.
python3 execution/cross_agent_context.py store \
--agent "<your-agent>" \
--action "Agent architecture designed — Control Tower + specialist agents with shared Qdrant memory" \
--project <project>
Control Tower Integration
Register agents and tasks with the Control Tower (execution/control_tower.py) for centralized orchestration across machines and LLM providers.
Blockchain Identity
Each agent has a cryptographic Ed25519 identity. All memory writes are signed — enabling trust verification in multi-agent systems.