Vision Capabilities¶

Overview¶

AIDDDMAP's vision system provides advanced computer vision capabilities for processing, analyzing, and understanding visual data. The system supports real-time object detection, scene segmentation, facial analysis, and custom vision models.

Core Features¶

1. Object Detection¶

Real-time object detection and tracking
Multiple object class support
Confidence scoring
Bounding box generation

2. Scene Segmentation¶

Semantic segmentation
Instance segmentation
Panoptic segmentation
Depth estimation

3. Facial Analysis¶

Face detection
Facial landmark detection
Expression analysis
Identity verification

4. Custom Vision Models¶

Model training interface
Transfer learning support
Model optimization
Deployment management

Implementation¶

1. Vision Configuration¶

interface VisionConfig {
  mode: VisionMode;
  models: ModelConfig[];
  processing: ProcessingConfig;
  output: OutputConfig;
}

interface ModelConfig {
  type: ModelType;
  weights: string;
  confidence: number;
  acceleration?: "CPU" | "GPU" | "NPU";
}

enum ModelType {
  DETECTION = "detection",
  SEGMENTATION = "segmentation",
  FACIAL = "facial",
  CUSTOM = "custom",
}

2. Object Detection¶

interface DetectionConfig {
  model: "yolov8" | "ssd" | "faster-rcnn";
  classes: string[];
  threshold: number;
  tracking?: TrackingConfig;
}

interface DetectionResult {
  objects: DetectedObject[];
  frame: number;
  timestamp: number;
  metadata: DetectionMetadata;
}

interface DetectedObject {
  class: string;
  confidence: number;
  bbox: BoundingBox;
  tracking?: TrackingInfo;
}

3. Segmentation¶

interface SegmentationConfig {
  type: "semantic" | "instance" | "panoptic";
  model: string;
  classes: string[];
  resolution: {
    width: number;
    height: number;
  };
}

interface SegmentationResult {
  masks: Mask[];
  classes: string[];
  scores: number[];
  metadata: SegmentationMetadata;
}

Vision Pipeline¶

1. Input Processing¶

interface InputProcessor {
  source: ImageSource;
  preprocessing: PreprocessingStep[];
  validation: ValidationRule[];
}

interface PreprocessingStep {
  operation: "resize" | "normalize" | "augment";
  parameters: any;
}

2. Model Inference¶

interface InferenceEngine {
  model: VisionModel;
  batch: BatchConfig;
  acceleration: AccelerationConfig;
}

interface VisionModel {
  architecture: string;
  weights: string;
  config: ModelConfig;
  optimization: OptimizationConfig;
}

3. Post-processing¶

interface PostProcessor {
  operations: PostProcessingOp[];
  filters: FilterConfig[];
  output: OutputFormat;
}

interface PostProcessingOp {
  type: string;
  parameters: any;
  order: number;
}

Integration Examples¶

1. Real-time Object Detection¶

// Configure real-time detection
const detector = new ObjectDetector({
  model: {
    type: "yolov8",
    weights: "yolov8n.pt",
    confidence: 0.5,
  },
  tracking: {
    enabled: true,
    algorithm: "sort",
    maxAge: 30,
  },
  processing: {
    width: 640,
    height: 640,
    fps: 30,
    batchSize: 1,
  },
  output: {
    format: "bbox",
    include: ["class", "confidence", "trajectory"],
  },
});

// Process video stream
const stream = await detector.processStream({
  source: videoStream,
  callback: (results) => {
    for (const obj of results.objects) {
      console.log(`Detected ${obj.class} with confidence ${obj.confidence}`);
    }
  },
});

2. Scene Understanding¶

// Configure scene analysis
const sceneAnalyzer = new SceneAnalyzer({
  segmentation: {
    type: "panoptic",
    model: "mask2former",
    classes: ["person", "car", "building", "road"],
  },
  depth: {
    enabled: true,
    model: "midas",
  },
  relationships: {
    enabled: true,
    maxObjects: 10,
  },
});

// Analyze scene
const analysis = await sceneAnalyzer.analyze({
  image: inputImage,
  requirements: {
    segmentation: true,
    depth: true,
    relationships: true,
  },
});

3. Custom Model Training¶

// Configure model training
const trainer = new VisionModelTrainer({
  architecture: {
    type: "detection",
    backbone: "resnet50",
    heads: ["bbox", "class"],
  },
  dataset: {
    train: trainDataset,
    val: valDataset,
    augmentation: {
      horizontal_flip: 0.5,
      rotation: 15,
      color_jitter: true,
    },
  },
  training: {
    epochs: 100,
    batchSize: 16,
    optimizer: {
      type: "adam",
      lr: 0.001,
    },
    scheduler: {
      type: "cosine",
      warmup: 5,
    },
  },
});

// Train model
const model = await trainer.train({
  checkpointing: {
    enabled: true,
    interval: 10,
  },
  validation: {
    interval: 5,
    metrics: ["mAP", "recall"],
  },
});

Performance Optimization¶

1. Hardware Acceleration¶

interface AccelerationConfig {
  device: "CPU" | "GPU" | "NPU";
  precision: "FP32" | "FP16" | "INT8";
  batchSize: number;
  threads: number;
}

2. Model Optimization¶

interface OptimizationConfig {
  quantization: QuantizationConfig;
  pruning: PruningConfig;
  distillation: DistillationConfig;
}

Best Practices¶

1. Model Selection¶

Choose appropriate architectures
Consider hardware constraints
Balance accuracy and speed
Regular model updates

2. Data Processing¶

Proper preprocessing
Robust augmentation
Efficient batching
Error handling

3. Deployment¶

Model optimization
Resource management
Monitoring
Version control

Future Enhancements¶

Planned Features
3D scene understanding
Multi-camera fusion
Advanced tracking
Real-time optimization
Research Areas
Few-shot learning
Self-supervised learning
Neural architecture search
Edge deployment