Build High-Speed, Precision Spatial Perception, and Edge-Ready Real-Time Object Detection Infrastructure.
Real-time spatial tracking, open-vocabulary zero-shot recognition, multi-object video stream analytics, and end-to-end NMS-free deployment engineered with RF-DETR, YOLO26, YOLOv12, RT-DETR, Grounding DINO, and TensorRT.
Why Object Detection for AI/ML & Data
pinpoint target coordinates, bounding boxes, and object classes instantly across single-frame scans and live multi-camera feeds
eliminate fragile Non-Maximum Suppression (NMS) post-processing steps using modern Real-Time Transformer architectures (RF-DETR, RT-DETR) and NMS-free CNN backbones (YOLO26) to achieve predictable latency
identify dynamic, un-trained, or long-tail categories on the fly using zero-shot vision-language detection architectures like Grounding DINO and YOLO-World
deploy low-footprint INT8/FP16 models natively to edge hardware (NVIDIA Jetson, Apple Neural Engine, ARM CPUs) at 100+ FPS without data center GPU dependency
What We Build With
Object Detection
Video Analytics & Tracking
multi-stream security systems, crowd density analysis, and spatial movement tracking integrating detection models with ByteTrack and BoT-SORT algorithms
Quality Inspection
Autonomous Mobility
Retail & Inventory Automation
Open-Vocabulary Text-Prompt AI
Object Detection & Data Expertise at StackDot
Where Object Detection Fits Best
Active Industry
Robotics & Autonomous Vehicle Spatial Perception
Why StackDot for Object Detection Engineering
Frontier Architecture Mastery
leveraging state-of-the-art NMS-free Real-Time Transformers (RF-DETR) and modern YOLO generations to maximize bounding box precision and frame rates
Proactive Hardware Compute & FinOps
optimizing model parameters, export formats, and quantization levels to dramatically lower cloud GPU costs and hardware overhead
End-to-End MLOps & Drift Monitoring
deploying containerized vision pipelines equipped with automated data drift detection, false positive monitoring, and seamless active learning re-training loops
Flexible Cloud, Edge, and Hybrid Deployments
engineering resilient vision microservices that run securely across cloud API clusters, enterprise on-premises nodes, or low-power embedded chips
Ready to Deploy Real-Time, High-Precision Spatial Perception With Enterprise Object Detection?
Real-time spatial tracking, open-vocabulary zero-shot recognition, multi-object video stream analytics, and end-to-end NMS-free deployment engineered with RF-DETR, YOLO26, YOLOv12, RT-DETR, Grounding DINO, and TensorRT.