Checking authentication...

Object Detection

Find and locate objects in images automatically. Detect products, people, vehicles, equipment, and more with bounding boxes that show exactly where objects are located.

Find things in images. Object detection identifies what objects are in an image and where they're located, making it perfect for inventory counting, safety monitoring, and quality control.

What is Object Detection?

Object detection identifies and locates objects in images by drawing bounding boxes around them. Unlike classification (which only tells you what's in an image), object detection tells you both what objects are present and where they're located.

Bounding Boxes

Objects are identified with rectangular bounding boxes that show their location and size. Each box is labeled with the object class and a confidence score.

Multiple Objects

Detect multiple objects of different types in a single image. Count objects, identify locations, and track multiple items simultaneously.

Real-Time Detection

Run object detection in real-time on mobile devices or edge hardware. Get instant results without sending images to the cloud.

Common Use Cases

Inventory Counting

Count products on shelves automatically. Take photos of shelves and get accurate product counts without manual counting.

PPE Detection

Detect personal protective equipment in safety inspections. Verify that workers are wearing required safety gear like hard hats, safety vests, and safety glasses.

Vehicle Identification

Identify and track vehicles in parking lots, loading docks, or traffic monitoring. Count vehicles, identify types, and monitor vehicle movement.

Product Detection

Detect products in retail environments. Identify products on shelves, verify product placement, and check for out-of-stock items.

Defect Detection

Detect defects in manufactured products. Identify scratches, cracks, dents, or other visual defects automatically.

People Counting

Count people in crowds, events, or facilities. Monitor occupancy, track foot traffic, and ensure capacity limits are met.

Accuracy and Performance

Object detection models can achieve high accuracy with proper training and data.

Model Accuracy

Well-trained object detection models can achieve 90%+ accuracy for common objects. Accuracy depends on training data quality, object complexity, and image conditions.

Confidence Scores

Each detection includes a confidence score showing how certain the model is about the detection. Use confidence thresholds to filter low-confidence detections.

False Positives and Negatives

Balance false positives (detecting objects that aren't there) and false negatives (missing objects that are there) based on your use case requirements.

Performance Optimization

Optimize models for speed or accuracy depending on your needs. Faster models work better for real-time applications, while more accurate models are better for quality control.

Training Object Detection Models

Train custom object detection models for your specific use case.

Data Collection

Collect images of objects you want to detect. Include objects in various conditions, lighting, and angles to improve model robustness.

Labeling

Label objects with bounding boxes using the mobile app or web interface. Draw boxes around objects and assign class labels.

Model Training

Train models using YOLO11 architecture optimized for object detection. Models train quickly and achieve high accuracy with relatively small datasets.

Model Evaluation

Evaluate models using precision, recall, and mAP (mean Average Precision) metrics. Test on validation data to ensure models perform well on new images.