Category: Eric’s Status Reports

This week, I mainly focused on refining and updating the design report, specifically working on the Use-Case Requirements, Architecture and/or Principle of Operation, and Design Requirements sections. Some specific changes I made are:

• Architecture and/or Principle of Operation: I refined the block diagram and system interactions, ensuring that the data flow from image capture → edge processing → cloud verification → database matching was clearly structured. I also improved the documentation/planning of how the edge processor filters and sends high and low-confidence detections to the cloud, reducing unnecessary bandwidth use.
• Design Requirements: The biggest change since the Design presentation was updating the cloud reliability target. After reviewing existing cloud service reliability standards, I adjusted the uptime requirement from 95% to 97% to strike a balance between AWS’s guaranteed availability and real-world reliability needs. This change ensures that the system remains operational and responsive in high-demand scenarios, reducing the likelihood of missed detections due to cloud downtime.

I also worked with Richard to further define how the cloud infrastructure handles license plate matching and how that would be implemented, specifically using Supabase and AWS Sagemaker. My progress is on schedule, and we have begun testing timing on the Rpi. Next week I plan to continue working with Richard on testing the models on the Rpi, and hopefully begin testing using images from the camera module.

This week, I spent a lot of time working on the design review presentation and practicing since I presented on Wednesday. This involved doing research related to the Amber alert use case, specifically for our timing requirements, since we wanted them to be based on the expected situation. I found that the 60 second requirement was sufficient for an average lane change frequency on the highway (2.71 mi) but not enough for the worst case merging scenario (20 seconds), so I made that change to the requirements.    I worked more with the PaddleOCR testing to continue exploring how it performs under more extreme weather conditions. I also worked with Richard to set up the basic pipeline of YOLOv11 to PaddleOCR, where YOLOv11 crops the image down to the plate, and PaddleOCR uses the cropped image to do OCR.

 

My progress is on schedule. Next week, I plan to continue testing the PaddleOCR with the YOLOv11 model integration, and explore methods to increase performance. I plan to use larger datasets to see how the overall pipeline performs, as well as beginning to check the inference time.

 

This week, I focused on researching and testing OCR models for license plate recognition. I experimented with PaddleOCR and EasyOCR, since I saw multiple users saying that TessaractOCR doesn’t perform well. I tested PaddleOCR’s and EasyOCR’s performance on license plates with different orientations and angles. To ensure accurate comparisons, I set up a structured testing workflow and collected sample images from various scenarios. After testing, I found that PaddleOCR consistently outperformed EasyOCR when handling rotated or slanted plates. Based on these results, I decided to move forward with PaddleOCR as the primary OCR engine for the project. I also started looking into ways to eliminate detected text that isn’t from the license plate number.

My progress is on schedule. Next week, I plan to work on integrating PaddleOCR with the YOLOv11 model, and figure out what changes are needed for it to run on the Raspberry Pi. If necessary, I will experiment with different PaddleOCR configurations to further refine accuracy and speed.

The image below shows PaddleOCR’s results on an example plate:

 

This week, I worked on the proposal presentation slides, conducted background research on license plate recognition methods, and explored available recognition models like OpenALPR, EasyOCR, and YOLO. I also examined competitors, including Genetec, PLATESMART MOBILE DEFENDER, and Nvidia Metropolis. I experimented with online available solutions and found that current methods usually involve several steps of narrowing down the image to the license plate before running OCR. For example, they would locate the car in the image, then the license plate, and then run the character recognition. In my research, I also discovered that OpenALPR, although free, has not been updated in 5-7 years and seems to have relatively poor performance compared to more modern alternatives.

My progress is on schedule, and next week I plan to work on the design proposal, research available and relevant datasets, and try the baseline yolov11 without fine tuning to see if license plates were already one of the classes in the training set and how it performs. I will also research different preprocessing techniques to improve recognition accuracy under varying conditions such as lighting and motion blur.