This document summarizes work on developing a smart parking solution using image recognition and instance segmentation. The team initially expected a pre-trained Mask R-CNN model to work well but results were poor. Through manual tuning of hyperparameters, image augmentation testing, and matching masked data to parking spots, performance improved. Key lessons were to not expect pre-trained models to transfer directly, favor simpler models, and generalize the solution to detect non-marked parking areas by identifying parking slot masks and car distances. The solution provides occupancy status classification of parking spots in images.

1. SMART PARKING
#coco #maskrcnn #image_recognition

2. Introduction

3. What’s already been done
SoftServe:
- cropping parking slots
- image classification on cropped images

4. Softserve Solution
FREE

5. Softserve Solution
OCCUPIED

6. Edge case

7. How we though it’s gonna go
- Counting cars with an instance segmentation
model
- Pre-trained model will give good results on our
data
- Bingo!

8. Of course, it didn’t go as planned 🙄

9. But we managed to fix it 💪

10. So what did we do?
- Pretrained Mask R-CNN - trained on COCO dataset - for
instance segmentation
- Manual hyperparameters tuning
- Several iterations of testing how image augmentation can
improve the results
- Matching masked data with the predefined parking spots
positions
COCO Dataset | Pretrained Mask RCNN by Visualbuffer

11. Mask RCNN Structure
Mask RCNN Paper | Open Source Mask RCNN Network Implemenation by Matterport

18. Free
Partially Occupied
Occupied

19. Partially
Occupied

20. VIDEO
🚨 Emergency Link to the video https://youtu.be/qcjsXZn8Bzk

21. FINAL RESULT
FINAL RESULT

22. What we learned?
- No high expectations for pre-trained models
- Occam's razor or go with the simplest model that does the
job
- Unix saves the day

23. Next steps
- Training a model for our specific case
- Generalizing solution further for non-marked parking areas:
- Masks for parkings slots detections
- Detecting distance between cars to estimate the
number of free slots

25. Mentors
Andriy KusyyYuriy Pryyma

26. Team
David Lackovic Liliia Taran Mykhailo Girnyak Polina Shevchenko

27. FINAL RESULT
FINAL RESULT