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` ## Limitations - Use this skill only when the task clearly matches the scope described above. - Do not treat the output as a substitute for environment-specific validation, testing, or expert review. - Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.