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Eric’s Status Report for 3/8/25

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.

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Eric’s Status Report for 2/22/25

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.

 

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Team’s Status Report for 2/15/25

The most significant risk is that the edge compute solution may not guarantee enough performance (precision and recall) to meet our MVP. The contingency plan is having a two-phase approach where if more accuracy than the edge compute raspberry pi can give us is needed, we then send the image into the cloud, where a more sophisticated model can give us better results.

A change we made to the existing design was that we are now using a Raspberry Pi 4 rather than a 5. This change was made since all the raspberry pi 5s available in storage were claimed very quickly, and since we wanted to test our software as soon as possible on actual hardware, we took a raspberry pi 4 instead. While unfortunate that we’re unable to use the most powerful hardware available, this should not have any impact on our ability to create an MVP or final device since the process for loading the models on these devices are nearly identical. When we run our model, if the performance is in the order of magnitude fitting of a compact processor, we can spend our currently plentiful remaining budget on the more powerful raspberry pi 5.

We have trained the model we will most likely use for our MVP, a YOLOv11n model trained on an open source license plate detection dataset for 400 epochs. It can be found here. We have also looked into existing OCR methods and chosen the PaddleOCR out of them, which we’re currently experimenting with.

Aside from the model, the rpi 4 is currently being developed, with a github repo to be populated by next week. The camera module is also expected to arrive next week as well.

Part A written by Richard Sbaschnig:

A.

Our device aims to improve public safety. This is done by detecting the license plates listed in active amber alerts in a dashcam. Since these alerts are sent to identify suspected kidnappers of children, by increasing the search coverage of amber alerts with our device, law enforcement will be able to find these vehicles sooner and catch kidnappers sooner. This should also have a deterrent effect, since would-be kidnappers would be less inclined knowing that there are these devices all around that can identify their car and notify the police automatically.

Part B written by Tzen-Chuen:

Our device’s social considerations don’t quite appear as an obvious point of concern. The different groups that will be interacting through our device is the manufacturer, the consumer, and the potential child abduction victim. The main point of contention may be between the consumer/end user and the manufacturer, as the manufacturer may install our device without the end user being aware of it, but I believe that this can be mitigated through an explicit opt-in system.

 

Part C written by Eric:

Our license plate recognition system is designed with one of the focuses being affordability, utilizing low-cost Raspberry Pis and camera modules. This provides a more accessible alternative to expensive surveillance systems, ensuring that even communities with limited resources can use our system. This can be especially beneficial for those in rural areas with little existing infrastructure since our device would be mounted as a dash cam, allowing for wider reach and greater impact.

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Eric’s Status Report for 2/15/25

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:

 

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Eric’s Status Report for 2/8/25

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.