Computer Vision Engineer - Industrial AI Automation

FARHAN

Computer Vision Engineer Industrial AI & Automation

About Me:

Professional Experience & Training:

Computer Vision Engineer (2024–Present) with 1+ year experience in object detection, OCR (PaddleOCR, EasyOCR, Tesseract), image segmentation (YOLOv8-seg, SAM), and industrial automation. Skilled in industrial camera integration (Pylon, IP, CPP Clus) and real-world deployment. Completed 1-year Data Science & AI/ML training

(2023–2024) at Ducat Institute, Noida covering Python, Machine Learning, Deep Learning, NLP, and Computer Vision. Skill:

Education Qualification:

**************@*****.*** +918*********

https://github.com/farhan0715

Projects:

www.linkedin.com/in/Farhan-Pasha

Template-Based OCR Industrial Label Verification System (Amber Jhajjar) Computer Vision & VLMs : Object Detection (YOLOv8–v11, RF-DETR, OBB), Vision Transformers (ViT), Vision- Language Models (Florence-2, Qwen-VL), Image Segmentation (SAM/SAM2, YOLO-Seg), Re-ID, ByteTrack. Document AI & OCR: PaddleOCR (Fine-tuned Custom Detector & Recognizer), Qwen OCR, Template Matching, Preprocessing & Text Correction Pipelines Model Optimization & Inference Acceleration: Quantization (INT8/FP16), TensorRT (GPU Acceleration), ONNX Runtime (CPU Optimization), OpenVINO

Deployment: Docker, Flask API, REST APIs, Linux (Ubuntu), Git/GitHub Industrial Vision & Hardware: Basler & Hikvision/Hikrobot Cameras (GigE/USB via Pylon & MVS SDK), IP/RTSP Cameras, Sensor Capture (Proximity/Hardware/Timer).

PLC Integration & Automation: Modbus/TCP, Register Communication, Zebra Barcode/QR Triggered Workflows, End-to-End Pipeline (Sensor/Barcode $\rightarrow$ Camera Trigger $\rightarrow$ AI Inference

$\rightarrow$ PLC OK/NG).

Backend, API & Edge Deployment: FastAPI, Flask, Model Serving Pipelines, Docker, Linux (Ubuntu), Git/GitHub, Roboflow.

Frameworks & Acceleration: PyTorch (CUDA/cuDNN GPU acceleration), TensorFlow, OpenCV, torchvision, Albumentations, NumPy, Pandas.

Assembly Verification System(amber jhajjar)

Description: Developed and deployed a real-time OCR system for industrial label verification. Integrated Basler Pylon cameras for image capture, used PaddleOCR, for text extraction, and implemented template-based matching to verify predefined fields against expected values. Displayed OK/NG status on a Flask-based dashboard for quality inspection. Technologies: Python, Flask, PaddleOCR, OpenCV, Basler Pylon SDK. Developed an automated assembly verification system using YOLOv8 to detect and verify the correct placement and sequence of components in industrial assembly lines. Implemented class-based object counting to ensure each class meets predefined counts, e.g., Class-1 requires 2 instances, Class-2 requires 4 instances.

Verified the sequence of components to ensure the correct assembly order. If the count and sequence matched predefined standards, the system displayed an 'OK' status; otherwise, it flagged 'NG' (Not Good). Enhanced quality control processes by identifying missing, misplaced, or defective parts with high accuracy. Integrated real-time detection capabilities with Pylon cameras for seamless industrial deployment. Technologies: YOLOv8, OpenCV, Python, Pylon Camera SDK Graduation: B.Tech(IT) Shree Siddhi Vinayak Institute of Technology, Bareilly 2019 – 2023 Industrial Certification in Data Science & AI Ducat Institute, Noida (ISO 9001:2000 Certified) 2024 Intermediate: JAIN INTER COLLAGE Rampur (2019)

High School: KISHORE INTER COLLAGE -Raipur Bilaspur(2017) Computer Vision Engineer :- Orama Solutions LLP from sep 2024 to till (2026 – Present) Computer Vision Engineer with hands-on experience designing end-to-end industrial inspection systems, real-time edge AI pipelines, and custom vision solutions. Specialized in object detection (YOLO, OBB), segmentation (SAM, YOLO-seg), Document AI/OCR(on machine parts comming on convayer) (PaddleOCR, Qwen Vision-Language Models), and real-time industrial integrations (Basler GigE/USB cameras, sensor triggering, PLC communication, and FastAPI). Proven track record of deploying robust automated quality control systems directly onto manufacturing lines.

Defect & Dent Segmentation System (Industrial Project -jagure) Description: Built an industrial defect detection system using YOLOv8-seg for pixel-level dent segmentation and SAM for automated dataset preparation, improving accuracy and reducing labeling time. Technologies: YOLOv8-seg, SAM, OpenCV, PyTorch.

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