- Developed advanced computer vision algorithms for performance in diverse and challenging conditions.- Managed end-to-end deep learning projects from data collection to model training, delivering robust solutions.- Engineered GPU-accelerated algorithms with CUDA to boost processing speeds and efficiency.- Developed image processing algorithms using traditional techniques for complex visual recognition.- Built and maintained scalable web services with Flask for… Show more - Developed advanced computer vision algorithms for performance in diverse and challenging conditions.- Managed end-to-end deep learning projects from data collection to model training, delivering robust solutions.- Engineered GPU-accelerated algorithms with CUDA to boost processing speeds and efficiency.- Developed image processing algorithms using traditional techniques for complex visual recognition.- Built and maintained scalable web services with Flask for efficient backend integration.- Proficient in OpenCV, Keras, TensorFlow, Darknet (YOLO), MediaPipe, Dlib, MongoDB, Flask, Git, Scikit-learn, and SQL/NoSQL databasesProjects and Responsibilities——— High Security Biometric Verification ———- Live Demo: https://smartir.io/facerecognition- Face Detection- Face Recognition (1:1 and 1:N identity matching)- Liveness Detection- Object Tracking- Developing algorithms that achieve high accuracy in poorly illuminated and low-resolution images- Working with depth camera data- Conducted rigorous validation of algorithms by benchmarking and testing against international standards to ensure top-tier performance and reliability in biometric verification.——— DMS (Driver Monitoring System) ———- Face Recognition- Pose Detection- Image Classification- Object Detection (e.g., cigarette, mobile phone, seatbelt)- Enhancing images to counter poor illumination conditions——— E-Sports Game Analysis ———Developed algorithms for analyzing player performance in Valorant:- Aim performance- Bullet usage and accuracy (shots fired and hit, hit locations)Analyzed player performance in CS:GO:- Detailed performance metrics- Visualization of players' and equipment positions on a 2D map——— MRZ Identification and Reading ———- Identification and reading of the Machine Readable Zone (MRZ) on ID cards- Handling issues related to shaking and blurring during image capture- Using specialized libraries for accurate MRZ detection Show less

Managing the data labeling team in the L4 Highway Auto-Pilot Project.

--- Lane and Drivable Area Segmentation and Object Detection/Classification in Highway Images ---- Developed Deep Learning algorithms for lane and drivable area segmentation, as well as traffic sign detection/classification using data collected and labeled by Ford Otosan.- Utilized technologies such as Python, Pytorch, Tensorflow, YOLOv4, and OpenCV.Key Responsibilities:- Generating masks from JSON file data- Testing the accuracy of generated masks- Creating and… Show more --- Lane and Drivable Area Segmentation and Object Detection/Classification in Highway Images ---- Developed Deep Learning algorithms for lane and drivable area segmentation, as well as traffic sign detection/classification using data collected and labeled by Ford Otosan.- Utilized technologies such as Python, Pytorch, Tensorflow, YOLOv4, and OpenCV.Key Responsibilities:- Generating masks from JSON file data- Testing the accuracy of generated masks- Creating and managing datasets- Data preprocessing and augmentation- Training segmentation models using SegNet- Training object detection models using YOLOv4- Training image classification models using CNN- Making predictions with test data Show less

Labeling and reviewing data used in Deep Learning models in L4 Highway Auto-Pilot Project.

--- Drivable Area Detection in Highway Images with Semantic Segmentation ---- Developed a Deep Learning algorithm for drivable area segmentation using data collected and labeled by Ford Otosan.- Utilized technologies such as Python, Pytorch, and OpenCV.Key Responsibilities:-Generating masks from JSON file data to identify pixels representing free space in the images.-Testing the accuracy of the generated masks.-Data preprocessing: Formatting images and masks as… Show more --- Drivable Area Detection in Highway Images with Semantic Segmentation ---- Developed a Deep Learning algorithm for drivable area segmentation using data collected and labeled by Ford Otosan.- Utilized technologies such as Python, Pytorch, and OpenCV.Key Responsibilities:-Generating masks from JSON file data to identify pixels representing free space in the images.-Testing the accuracy of the generated masks.-Data preprocessing: Formatting images and masks as features and labels for segmentation, suitable for feeding into the PyTorch segmentation model.-Performing data augmentation.-Training a segmentation model using U-Net.- Making predictions with test data. Show less