Presear engineers production computer vision systems — object detection, semantic segmentation, video analytics, OCR, and 3D reconstruction — for industrial, medical, and retail applications.
Technical Depth
From real-time object detection to 3D scene reconstruction — we apply the right vision architecture for your application, hardware, and accuracy requirements.
Localising and classifying multiple objects simultaneously in images and video streams — from single-stage detectors (YOLO, DETR) for real-time edge deployment to two-stage models (Faster R-CNN) for maximum accuracy. We also build multi-object tracking pipelines (ByteTrack, DeepSORT) for persistent identity across video frames.
Pixel-level understanding of scenes — classifying every pixel (semantic segmentation) or distinguishing individual object instances (instance segmentation) for precise boundary delineation. We use Mask R-CNN, Detectron2, Segment Anything (SAM), and nnU-Net for medical images, deploying optimised variants at production inference speed.
End-to-end document text recognition — detecting and reading printed, handwritten, and stylised text from images of documents, signs, forms, and product labels. We build pipelines that handle skew correction, layout analysis, table extraction, and multi-language OCR with domain-specific post-processing for downstream structured data extraction.
Estimating human and object keypoint configurations from images and video for action recognition, safety monitoring, ergonomics analysis, and motion capture. We use HRNet, MediaPipe, and ViTPose architectures for 2D/3D pose estimation and SlowFast or Video Swin transformers for temporal action classification.
Processing depth images, LiDAR point clouds, and stereo camera data for 3D object detection, scene reconstruction, and spatial mapping. We build PointNet, VoxelNet, and PointPillars pipelines for autonomous vehicle perception, robotic grasping, and industrial 3D quality inspection — with real-time optimised deployment on embedded hardware.
Extracting temporal intelligence from video streams — detecting events, counting, tracking, and identifying anomalous behaviour without requiring labeled anomaly examples. We build unsupervised and semi-supervised anomaly detection systems using reconstruction-based autoencoders and predictive frame models for industrial and security applications.
Our Process
A rigorous five-stage process. Click any step to explore what happens — and why it matters.
Computer vision model quality is fundamentally bounded by annotation quality. We design labelling workflows, build annotation pipelines using Label Studio and Roboflow, and manage quality control processes — including inter-annotator agreement checks and active learning loops to prioritise the most informative samples for labelling budget efficiency.
Architecture choice in computer vision has outsized impact on both accuracy and inference cost. We evaluate backbone options (ViT, ConvNeXt, EfficientNet), task heads, and neck designs against your specific trade-off requirements — running small-scale architecture ablations before committing to full-scale training to avoid expensive dead-ends.
Vision model training requires domain-specific augmentation strategies that reflect real deployment conditions — lighting variation, occlusion, viewpoint change, scale variation, and sensor noise. We design augmentation pipelines (Albumentations, imgaug) tailored to your application context, significantly improving generalisation beyond the training distribution.
Computer vision systems have dual requirements: accuracy (mAP, IoU, dice coefficient) and speed (FPS, latency per frame). We profile both comprehensively on target hardware — GPU, CPU, and embedded SoC — and surface the accuracy-latency trade-off curve to help you make an informed deployment decision before any production commitment.
Vision models must be optimised differently for edge vs. cloud. For edge (Jetson, RK3588, mobile), we apply TensorRT compilation, INT8 quantisation, and model pruning to meet sub-30ms latency targets. For cloud, we use NVIDIA Triton with dynamic batching for high-throughput batch and stream inference — containerised and autoscaled on Kubernetes.
Real-World Impact
Production vision systems deployed across industries — seeing, measuring, and deciding at machine speed with human-level accuracy.
Core Challenge
Manual visual inspection on production lines is inconsistent, fatigues over time, and cannot operate at line speeds above a few hundred parts per minute. Surface defects, dimensional deviations, and assembly errors pass through at low inspection rates — causing downstream quality escapes and costly recalls.
Who Benefits
Electronics manufacturers, automotive parts suppliers, pharmaceutical packaging lines, and FMCG producers that need inline visual inspection at production line speeds — detecting surface defects, foreign objects, label errors, and dimensional deviations without human operator involvement.
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Radiologists reviewing high volumes of CT, MRI, and X-ray images face mounting fatigue and inconsistency — particularly for subtle findings like early-stage lung nodules, micro-calcifications, and small lesions that require sustained expert attention to detect reliably. AI-assisted analysis augments radiologist throughput and consistency.
Who Benefits
Radiology departments, diagnostic imaging centres, tele-radiology platforms, and medical device companies that need AI-assisted triage — flagging high-priority cases, measuring lesion size, segmenting organs, and quantifying disease progression for treatment monitoring.
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Retailers lose significant revenue to out-of-stock, misplaced, and incorrectly priced products that staff miss during manual shelf audits. Manual walkthrough checks are infrequent, inconsistent, and cannot scale to the thousands of SKUs and shelf sections in a large store or chain — making continuous compliance monitoring impossible without automation.
Who Benefits
Grocery chains, CPG brands, pharmacy retailers, and convenience store operators that need real-time shelf monitoring — detecting out-of-stocks, planogram deviations, price tag discrepancies, and product misplacements — delivered through existing store camera infrastructure.
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Urban planners and transport operators lack real-time, accurate data on vehicle flows, pedestrian density, and congestion patterns — relying on infrequent manual counts or coarse sensor loops. Computer vision on existing CCTV infrastructure provides continuous, granular spatial intelligence without deploying additional sensors.
Who Benefits
City transport authorities, airport operators, event venue managers, and urban planners that need real-time crowd density estimation, vehicle classification, queue length monitoring, and anomaly detection — integrated with traffic management and safety alert systems.
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Best-in-class detection frameworks, annotation tools, inference runtimes, and deployment platforms — from research to production.
Frequently Asked
Answers to the questions engineering leaders, operations teams, and product managers ask before starting a CV engagement with Presear Softwares.
Ask Our CV TeamPartner with Presear Softwares to build computer vision systems that go beyond proof-of-concept — benchmarked rigorously, optimised for your hardware, and designed to deliver measurable value from day one.