License Plate Recognition by Raspberry PI

 

In Week 4, we faced major challenges in Optical Character Recognition (OCR) for license plates. The initial OCR strategy delivered poor results due to multiple factors, including motion blur, occlusion, perspective distortion, and character misalignment. To overcome these issues, we implemented license plate character correction, refined segmentation techniques, and integrated more advanced text detection models. Eventually, we selected the CRAFT model for robust text detection and further optimized our pipeline to ensure high accuracy. Additionally, we extended our work to video processing to enable real-time application. Challenges in OCR and Initial Solutions 1. Factors Affecting OCR Performance Our initial OCR implementation struggled due to: Blurriness and Occlusion : Vehicles at varying distances caused blurred license plate images, while partial occlusions further degraded recognition accuracy. Perspective Distortion : Different camera angles led to skewed and curved ...

 In week 3, we successfully resolved the issue of accessing the Raspberry Pi desktop. Additionally, we started training a license plate detection model using YOLO, specifically YOLOv8, to improve our system's accuracy. This week's progress included preparing the dataset, training the model, analyzing the results, and implementing Non-Maximum Suppression (NMS) to refine detection Lab Day: Resolving Raspberry Pi Desktop Access SSH Connection: First, we connected to the Raspberry Pi system using SSH, allowing us to access its terminal remotely. VNC Viewer Setup: After setting up SSH access, we used VNC Viewer to remotely control the Raspberry Pi's desktop interface, which enable us to manage our training tasks efficiently. Training the License Plate Detection Model Choosing YOLO for Object Detection We explored different object detection models and decided on YOLO due to its real-time efficiency and high detection accuracy.  Data Preparation and Training We collected and pre...

 In week 2, we finally get the equipment(Raspberry PI, LCD screen. camera, SD card) for our project, but we encountered multiple challenges, including SD card burning failures and difficulties accessing the Raspberry Pi system desktop. At the same time, we began transitioning from traditional computer vision methods to deep learning for improved license plate recognition accuracy. Lab Day Using Raspberry PI pin map and LCD pin layout as guide, we built the circuit. Challenges and Issues Faced SD Card Burning Failure One of the major obstacles was the failure to burn the operating system onto the SD card. Several attempts were made using different SD cards and flashing software such as Balena Etcher and Raspberry Pi Imager. The common issues encountered included: SD card not being recognized by the system. Flashing process getting stuck midway. Errors upon booting the Raspberry Pi after flashing. To address these issues, we tested multiple SD cards, reformatted them using different ...

Allocation of Roles and Analysis: During the initial week of our project, our team held a discussion to delegate specific roles and responsibilities to each member. After that, we defined the project requirements and outlined the software architecture. Lab Day: Today, our original task was to solve the hardware problem, but when we arrived at the lab, we found that we had no materials for our group, so we gave priority to the research and discussion of the software problem. The components we needed included: So we shifted our focus this week to software. We successfully implemented the core functionality of the recognition algorithm and completed the coding phase. However, during testing, we encountered some problems. In the first week of our project, we focused on developing a license plate recognition system using traditional computer vision techniques. Our initial approach relied on OpenCV, a widely used library for image processing. Instead of utilizing deep learning models, we opt...