alexander-kastil/computer-vision-expert

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