| # |
Date |
Planning for the week |
Progress
|
| 1 |
01.01.2026 |
- Plan bachelor thesis with Gantt-chart.
- Register to MATLAB Grader.
- Understand requirements and set up SVN.
|
- Created Gantt chart (Timeline: Jan–Apr).
- Analyzed requirements and set up development environment.
|
| 2 |
08.01.2026 |
- Solve Image Processing Tasks (Restoration & Enhancement).
- Implement Wiener Deconvolution and CLAHE.
|
- Implemented Wiener Deconvolution (deconvwnr) to recover original images from degradation.
- Applied CLAHE (Contrast-Limited Adaptive Histogram Equalization) to improve visibility.
- Saved results to SVN.
|
| 3 |
15.01.2026 |
- Solve Image Processing Tasks (Noise & Segmentation).
- Implement Median Filtering and K-Means.
|
- Utilized Median Filtering (medfilt2) to remove salt-and-pepper noise.
- Applied K-Means Clustering (imsegkmeans) to partition images based on pixel values.
|
| 4 |
22.01.2026 |
- Finalize Classic Algorithms (Model Segmentation & Classification).
- Document progress in Wiki.
|
- Implemented Active Contours (Snakes) for specific object isolation.
- Developed Rule-Based Shape Analysis (regionprops) to classify objects (e.g., Square vs. Circle) without ML.
- Completed Task 1 (Classic Algorithms).
|
| 5 |
29.01.2026 |
- Research on AI Solutions.
- Literature review on Deep Learning Architectures.
|
- Conducted comparative analysis on DL Architectures:
- ResNet (Standard)
- MobileNetV2 (Edge Efficiency)
- EfficientNet (Scalability)
- Vision Transformers (ViT)
|
| 6 |
05.02.2026 |
- Implement AI Image Processing (Classification).
- Design CNN architecture.
|
- Designed a **Sequential CNN** with a convolutional base and dense classification layer.
- Successfully classified low-resolution color images into 10 distinct classes (airplanes, birds, etc.).
|
| 7 |
12.02.2026 |
- Research Object Detection (YOLO).
- Data Collection and Labeling.
|
- Selected YOLOv11 (Ultralytics) for the LEGO Parts Detection System.
- Collected custom dataset (~400 images).
- Labeled dataset (bounding boxes) for LEGO bricks.
|
| 8 |
19.02.2026 |
- Train and Deploy YOLO Model.
- Run inference on test images.
|
- Implemented the YOLOv11 Model using Google Colab and NVIDIA GPU acceleration.
- Trained the model on the custom LEGO dataset.
- Successfully ran inference: The system automatically identifies and classifies LEGO bricks in new images.
|