Object Detection

AI-powered detection and localization of multiple objects in images and videos for automation.

Business impact

• Detection accuracy — Improves precision in identifying and localizing objects, reducing false positives
• Operational efficiency — Speeds up processes by automating object recognition and reducing manual tasks
• Safety incident rate — Lowers accidents by enabling early hazard detection and situational awareness
• Cost reduction — Decreases maintenance and operational costs through automation and edge processing

Data requirements

• Video feeds from cameras (Video) — Provide real-time visual data for detecting and tracking objects
• LiDAR sensor data (Numeric) — Supply 3D spatial information to enhance object localization and mapping
• Infrared and RGB images (Image) — Enable detection under varying lighting and environmental conditions
• Annotated training datasets (Structured) — Used to train supervised deep learning models for object classification

AI methods and techniques

• Predictive AI — Used to forecast object presence and location based on learned patterns
• Generative AI — Employed for data augmentation and synthetic training data generation
• Symbolic AI — Applied for rule-based post-processing and decision logic integration

AI models and model families

YOLOv10, Faster R-CNN, EfficientDet, Florence-2, Gemma 4, MambaNeXt-YOLO

Industries

Real-world evidence

24 documented case studies on record.

Companies using this: Aeva Technologies, Carroll Technologies Group, Excel Sense, Guilin University Electronic Technology, Hesai Technology, Innoviz Technologies, Luminar Technologies, NVIDIA, Nan Jing Agricultural University, Ouster, Ritsumeikan University, Robo Sense, SEA AI, SICK, Safe Pro Group Inc and 9 more.