{"id":19987267,"url":"https://github.com/rishn/cognitive-robotics-pen-detection","last_synced_at":"2025-03-01T21:17:32.036Z","repository":{"id":261247996,"uuid":"883738254","full_name":"rishn/Cognitive-Robotics-Pen-Detection","owner":"rishn","description":null,"archived":false,"fork":false,"pushed_at":"2024-11-05T15:11:25.000Z","size":29910,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-12T11:44:20.539Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/rishn.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-11-05T13:43:35.000Z","updated_at":"2024-11-05T15:11:29.000Z","dependencies_parsed_at":null,"dependency_job_id":"cf9f1d85-0a00-4a62-a5d6-3cf65dad7322","html_url":"https://github.com/rishn/Cognitive-Robotics-Pen-Detection","commit_stats":null,"previous_names":["rishn/cognitive-robotics-pen-detection"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rishn%2FCognitive-Robotics-Pen-Detection","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rishn%2FCognitive-Robotics-Pen-Detection/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rishn%2FCognitive-Robotics-Pen-Detection/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rishn%2FCognitive-Robotics-Pen-Detection/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/rishn","download_url":"https://codeload.github.com/rishn/Cognitive-Robotics-Pen-Detection/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241424468,"owners_count":19960752,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-13T04:35:04.153Z","updated_at":"2025-03-01T21:17:32.006Z","avatar_url":"https://github.com/rishn.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Cognitive Robotics Pen Detection\n\n## Aim\nTo build and evaluate an object detection system utilizing the YOLO (You Only Look Once) model for real-time object detection.\n\n## Components\n- **Dataset**: Images captured using a Raspberry Pi camera.\n- **CNN Model**: YOLO model for object detection.\n\n## Overview\nThis project leverages the YOLO model for detecting objects in images. The model is trained on a dataset created using a Raspberry Pi camera, capturing images of objects that are then annotated and processed for training.\n\n## Procedure\n1. **Image Capture**: Capture images using a Raspberry Pi camera and the `libcamera` module.\n2. **Annotation and Dataset Preparation**: Use Roboflow to annotate the images and prepare the dataset.\n3. **Model Training**: Utilize the YOLO model to train on the prepared dataset.\n4. **Prediction**: Run the model on test images to detect and visualize objects.\n\n## Running the Code\nTo execute the project, follow the steps outlined in the provided Jupyter notebook. You can also run the notebook in Google Colab for easy access and execution.\n\n## Results\nThe YOLO model successfully detects objects, and the results are visualized with bounding boxes on images. The following performance metrics were observed:\n- **Precision**: 0.80\n- **Recall**: 0.67\n- **Accuracy**: 0.57\n\n### Confusion Matrix\n|                         | Predicted: No Object | Predicted: Object |\n|-------------------------|----------------------|--------------------|\n| **Actual: No Object**   | 4                    | 15                |\n| **Actual: Object**      | 18                   | 0                 |\n\n### Example Outputs\nHere are some examples of the model's predictions:\n\n\u003cimg src=\"pred1.png\" alt=\"Predicted Image 1\" width=\"500\"/\u003e\n\u003cimg src=\"pred2.png\" alt=\"Predicted Image 2\" height=\"700\"/\u003e\n\u003cimg src=\"pred3.png\" alt=\"Predicted Image 3\" width=\"500\"/\u003e\n\n## Conclusion\nThis project demonstrates the capabilities of the YOLO model for real-time object detection, showcasing its effectiveness in visualizing detected objects with bounding boxes.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frishn%2Fcognitive-robotics-pen-detection","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frishn%2Fcognitive-robotics-pen-detection","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frishn%2Fcognitive-robotics-pen-detection/lists"}