I'm Vedant Sanjay Chavan – a passionate AI Engineer with expertise in Machine Learning, Deep Learning, Computer Vision, and Generative AI. With hands-on experience at HELLA GmbH & Co. and a strong academic background (M.Eng. in Mechatronics and B.Tech. in Mechanical), I blend creativity with technical excellence to transform complex data into real-world solutions.
Extracted multi-level features from MobileNetV3 and computed Mahalanobis-based anomaly scores at pixel level.
Exported the feature extractor to ONNX and performed inference using ONNX Runtime for cross-platform compatibility.
Visualized anomalies with heatmaps and binary masks using images from the MVTec AD dataset.
U-Net for Biological Image Segmentation
Trained a U-Net model on fluorescence microscopy images to segment nuclei structures with high accuracy.
ColabPyTorchSegmentationMatplotlib
Parsed and preprocessed instance masks into binary labels using CLAHE and Gaussian blur.
Built a lightweight U-Net in PyTorch; trained for 100 epochs on nuclei segmentation dataset.
Achieved Dice score of 0.89 and IoU of 0.82 on the validation set.
Visualized predicted masks and prepared inference-ready model using Colab.
Conversational AI Chatbot with RAG & LLM Fine-Tuning
Developed a GPT-based conversational AI assistant, integrated FAISS vector search, and deployed using Docker & Hugging Face Spaces.
Hugging Face / TransformersFAISSRAG
GaugeVision
The goal is to simplify gauge monitoring and tagging by using YOLOv11 pose model and QR detector.
YOLOv11OpenCVRoboFlow
Trained a custom YOLOv11 pose model to detect keypoints on analog pressure gauges.
Integrated OpenCV's QR code detector to read identification tags placed on or near gauges.
Combined both outputs visually to show needle direction and QR data in one intuitive display.
Edge Detector GUI (Qt + OpenCV + C++)
Built an interactive desktop app using Qt and OpenCV for visualizing Sobel and Canny edge detection with real-time threshold sliders.
OpenCVQTC++
Integrated OpenCV with Qt to enable real-time image processing using Sobel and Canny filters.
Added dynamic UI with sliders and dropdowns for live threshold tuning and filter selection.
Designed clean modular C++ logic with CMake for cross-platform build compatibility.
BLIP-1 Optimization for Efficient Image Captioning
Optimized BLIP-1 image-captioning on CPU via dynamic quantization and pruning, achieving 1.42× speedup with no accuracy loss.
Quantization L1 pruning
Baseline BLIP-1 CPU inference in 0.69 s for correct captions.
Applied dynamic quantization to all Linear layers, reducing latency to 0.48 s.
Combined 30% L1 pruning + quantization for a 1.40× speedup.
Preserved caption quality (“a man and his dog”) across all variants.
Stereo Point Cloud Reconstruction
Built a stereo vision pipeline for 3D point cloud reconstruction, achieving dense 3D models using SGBM and filtering.
OpenCV3D VisionStereo MatchingCalibration
Developed a robust stereo vision pipeline using OpenCV to compute disparity maps and reconstruct 3D point clouds from stereo images, improving depth estimation accuracy.
Optimized disparity computation by fine-tuning SGBM parameters and applying filtering techniques, reducing noise and enhancing 3D reconstruction quality.
Generated and exported point clouds in PLY format, enabling seamless visualization and analysis in 3D modeling applications.
Defect Detection in Prints Using U-Net
Implemented a U-Net model for defect detection, achieving 95% precision and reducing manual inspection time by 50%.
Defect injectionscikit-imageAugmentation
Implemented a U-Net-based model for defect detection, achieving 95% precision in quality assurance.
Built an AI pipeline processing 1000+ images per day, enhancing data throughput by 25%.
Automated defect detection reporting, delivering real-time insights to quality control teams.
Predictive Maintenance Using XGBoost
Built a predictive maintenance model using XGBoost, achieving high accuracy in failure prediction for industrial systems.
XGBoost
XGBoostPythonData filtering
Developed an end-to-end predictive maintenance model leveraging XGBoost for industrial failure prediction.
Achieved 98% accuracy by tuning model hyperparameters and implementing advanced analytics.
Automated reporting and insights generation, enhancing operational efficiency.
Reinforcement Learning for RRR Robot
Implemented a RL model for end effector path planning, optimizing robot arm trajectories in a 3D environment.
Reinforcement Learning
MATLABPythonKinematics
Developed and implemented RL-based trajectory optimization in MATLAB, improving end-effector accuracy by 30% in reaching target positions.
Trained the robot over 1,000 episodes using reward-based learning, reducing path deviation by 25% and ensuring efficient motion planning.
Simulated and validated RL policies in MATLAB, achieving an 85% success rate in autonomously selecting optimal paths for task execution.
ECG Signal Classification with AI
Developed a deep learning model for classifying ECG signals, achieving 99% accuracy in detecting cardiac anomalies.
ECG / BiosignalsMATLABSignal Processing
Developed a deep learning model in Python for ECG signal classification, achieving 99% accuracy in detecting abnormal heart rhythms.
Preprocessed and segmented raw ECG data, improving model training efficiency by 40% through feature extraction and noise reduction.
Optimized model inference speed, reducing prediction time by 30%, enabling real-time arrhythmia detection for potential clinical applications.
