Developing an AI-based automated construction progress tracking system for a solar plant is a complex project that involves various components, including computer vision, data analysis, and integration with existing construction management systems. Here's a step-by-step plan through the development process:
Main features of the application:
Work in progress
Reality capture
Schedule
Budget
Quality check
Steps we need to follow:
1. Define Project Objectives and Scope:
- Clearly define the goals and objectives of your construction progress tracking system.
- Identify the specific metrics and key performance indicators (KPIs) you want to track, such as completion of solar panel installation, infrastructure construction, and overall project progress.
2. Data Collection and Infrastructure Setup:
- Set up the necessary infrastructure for data collection, storage, and processing. This includes setting up cameras and sensors at key construction sites.
- Ensure a robust network connection to transmit data to a centralized server.
3. Computer Vision and Image Data Processing:
- Implement computer vision algorithms to analyze images and videos from construction sites.
- Develop image recognition models to identify construction equipment, workers, and project milestones.
- Use deep learning techniques to detect and track the progress of solar panel installation and other construction activities.
4. Data Annotation and Training:
- Annotate a dataset of images and videos to train your AI models.
- Train your models to recognize specific objects, actions, and construction milestones.
- Fine-tune the models to improve accuracy.
5. Real-time Monitoring and Tracking:
- Set up real-time monitoring of construction sites using cameras and sensors.
- Continuously analyze the data and images to detect anomalies or delays in the construction progress.
- Implement algorithms to track and predict construction milestones and timelines.
6. Data Integration:
- Develop APIs and data pipelines to integrate the AI-based progress tracking system with existing construction management software and databases.
- Ensure that relevant project data is accessible to project managers and stakeholders.
7. Dashboard and Reporting:
- Create a user-friendly dashboard for project managers and stakeholders.
- Provide real-time updates on construction progress, timelines, and KPIs.
- Implement reporting and alerting mechanisms for deviations from the planned schedule.
8. Verification and Validation:
- Conduct thorough testing of the AI system to ensure accuracy and reliability.
- Validate the system's performance against historical construction project data.
9. Deployment and Scaling:
- Deploy the system at one or more solar plant construction sites.
- Monitor system performance and scalability to ensure it can handle multiple projects simultaneously.
10. Continuous Improvement:
- Collect feedback from users and stakeholders to identify areas for improvement.
- Continuously refine the AI models and algorithms to enhance accuracy and efficiency.
- Stay updated with the latest advancements in computer vision and AI technologies to incorporate improvements.
11. Maintenance and Support:
- Provide ongoing maintenance and support for the system to address issues and ensure it remains operational.
12. Compliance and Security:
- Ensure data privacy and security compliance.
- Implement access controls and encryption to protect sensitive project data.
Main technologies will be used:
Machine Learning
Computer Vision, segment anything from Meta
Automated Planning and Scheduling
To monitor the everyday progress of a solar plant, you can use a combination of time-lapse photography, drone imagery, and fixed cameras. Since a single row of a solar plant can be quite long (up to 200 meters), capturing the entire row within a single image may not be feasible. Instead, you may need to take a more segmented approach. Here's how you can capture and process images for progress monitoring:
1. Fixed Cameras:
- Install fixed cameras at key vantage points within the solar plant. These cameras can capture high-resolution images of specific areas or sections of the solar plant.
2. Drones:
- Utilize drones equipped with cameras to capture aerial imagery of the entire solar plant. Drones can cover larger areas quickly and provide a top-down view, allowing you to assess progress across rows.
3. Time-Lapse Photography:
- Set up cameras to capture time-lapse images at regular intervals, such as daily or weekly. Time-lapse photography can help create a historical record of construction progress.
4. Segmentation and Stitching:
- Given the long row of the solar plant (up to 200 meters), you may need to capture it in segments or smaller sections.
- Use image stitching techniques to combine images from different angles and cameras to create a comprehensive view of the entire row. Software like Adobe Photoshop or specialized stitching software can assist with this.
5. Data Synchronization:
- Ensure that all cameras, drones, and time-lapse cameras are synchronized in terms of time and location. This synchronization is critical for accurate progress tracking.
6. Image Processing and Analysis:
- Develop or deploy the AI-based image analysis system to process the captured images. This system should identify and track the progress of solar panel installation, infrastructure construction, and other relevant activities.
7. Inference with Model:
- When ingesting video data, you can break the video into frames. For a solar plant row spanning 200 meters, you may need to segment the video into shorter clips (e.g., 10-20 meters) to ensure manageable data sizes.
- Process each frame or clip with your AI model to detect and track construction progress.
- The AI model can analyze each frame, identifying objects, workers, and construction milestones.
8. Real-time Monitoring:
- Implement real-time monitoring and reporting of construction progress. The AI model can provide updates on the status of each segment and the entire row.
9. Historical Data:
- Maintain a database of historical data and images, allowing you to compare the current status with past milestones.
10. Notifications and Alerts:
- Set up notifications and alerts for project managers or relevant stakeholders if the AI system detects anomalies or significant delays.
11. User Interface:
- Create a user-friendly dashboard for stakeholders to access real-time and historical data. The dashboard should display progress metrics, images, and videos.
Capturing the image to detect progress using a drone for daily surveys is cost-prohibitive, you can leverage field or construction personnel to capture video by walking through a row at a time. This approach can still provide valuable data for progress tracking. Here's how you can implement this alternative method:
1. Assign Field Personnel:
- Select and train field personnel responsible for capturing daily or periodic videos of specific rows within the solar plant. Ensure they understand the specific requirements for video capture.
2. Video Capture Process:
- Field personnel should walk through each designated row while recording video using a smartphone or other recording equipment.
- The video should capture the entire row or a significant portion of it, ensuring visibility of key construction activities and milestones.
3. Data Upload and Management:
- Create a structured system for field personnel to upload the captured videos to a central database or cloud storage.
- Each video should be tagged with relevant information, such as the date, time, row number, and any other necessary metadata.
4. Video Segmentation:
- As the videos are captured and uploaded, you may need to segment them into smaller clips, especially if a single video covers a long row.
- Segmentation helps in efficient processing and analysis.
5. AI-Based Progress Tracking:
- Implement an AI-based image analysis system to process the uploaded videos or video clips.
- The AI model should be capable of analyzing the videos, detecting construction activities, and tracking progress milestones.
6. Real-time Monitoring and Reporting:
- Develop a system for real-time monitoring and reporting of construction progress using the data extracted from the videos.
- The AI system should provide insights into the status of each row, flagging any anomalies or delays.
7. User Interface:
- Create a user-friendly dashboard for project managers and stakeholders to access progress data, images, and video clips.
- The dashboard should display the status of each row and provide historical data for comparison.
8. Quality Assurance:
- Implement a quality assurance process to ensure the videos captured by field personnel are clear, consistent, and meet the necessary standards for analysis.
9. Feedback and Communication:
- Maintain clear communication channels with the field personnel to address any issues, provide guidance, and ensure the data collection process runs smoothly.
Applications parts:
The backend REST API serverless application can run in Azure Functions. Which will be a batch processing application. A user will upload videos of each row to evaluate the progress of construction and save the data into a common database in the cloud.
Application for construction progress dashboard with all kinds of charts and reports to show the progress, cost and time to complete prediction.
it needs to input each device/module expected setup date and timeline
compare and find the progress and expected completion of the project
show all kinds of chart, graphs, require explaining the progress with help of numpy, pandas, ploty, matplotlib, seaborn
ML segmentation model with https://segment-anything.com/ [meta] which will find the segmentation and all the devices that are there in an image. Which will be generated from the row video.
after segmentation, each image needs to be inference by each CNN model one after another to identify the object eg. tracker or python clip
we can follow the steps to combine them as described here https://deci.ai/blog/image-segmentation-using-yolo-nas-and-segment-anything/
Time series analysis to automated planning and scheduling optimization
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