Object Detection

---
name: Object Detection/Segmentation Skill
description: Deep learning based object detection and segmentation for robotics applications
slug: object-detection
category: Perception
allowed-tools:
  - Bash
  - Read
  - Write
  - Edit
  - Glob
  - Grep
graph:
  domains: [domain:robotics]
  specializations: [specialization:robotics-simulation]
  skillAreas: [skill-area:motion-planning, skill-area:sensor-fusion]
  roles: [role:research-engineer]
---

# Object Detection/Segmentation Skill

## Overview

Expert skill for deploying and optimizing deep learning models for object detection, instance segmentation, and 3D object detection in robotics applications.

## Capabilities

- Configure YOLO (v5, v8) for real-time detection
- Set up Detectron2 for instance segmentation
- Implement semantic segmentation models
- Configure TensorRT optimization for Jetson
- Set up ONNX runtime deployment
- Implement 3D object detection (PointPillars, VoxelNet)
- Configure depth-based object detection
- Set up ROS vision pipelines with image_pipeline
- Implement object tracking (SORT, DeepSORT, ByteTrack)
- Configure multi-camera detection fusion

## Target Processes

- object-detection-pipeline.js
- synthetic-data-pipeline.js
- nn-model-optimization.js
- moveit-manipulation-planning.js

## Dependencies

- YOLO (Ultralytics)
- Detectron2
- TensorRT
- ONNX Runtime
- vision_msgs

## Usage Context

This skill is invoked when processes require object detection model deployment, instance segmentation, 3D detection, or multi-object tracking for robot perception.

## Output Artifacts

- Detection model configurations
- TensorRT optimized models
- ROS detection node implementations
- Tracking pipeline configurations
- Multi-camera fusion setups
- Inference optimization scripts