The YOLO (You Only Look Once) model is a powerful tool utilized in detecting objects, specifically designed for recognizing fire and gun-related incidents. In this article, we will guide you through the process of using the yolo.py script to enable real-time detection using configurable parameters.

Getting Started with yolo.py

To utilize the yolo.py script efficiently, you’ll need to be familiar with the command-line interface and understand the various arguments that can be passed. Here’s a breakdown of how to use it:

• -h, –help: Displays the help message and exits.
• –webcam WEBCAM: Set to True or False to toggle webcam usage.
• –play_video PLAY_VIDEO: Set to True or False to play a video.
• –image IMAGE: Specify an image file to detect objects.
• –video_path VIDEO_PATH: Provide the path to the video file for detection.
• –image_path IMAGE_PATH: Supply the path of an image to detect objects.
• –verbose VERBOSE: Set to True to print additional output statements during execution.

Setting Up Your Environment

Before you can run the detections, ensure that you have downloaded the necessary weights file. If the GitLFS file is not accessible, you can download the weights from the following link: Weights. Make sure to keep this file in your project folder.

Executing the Detection Script

To run the detection on a video, navigate to your project folder using the terminal. Use the following command to execute the script:

python yolo.py --play_video True --video_path videos/fire1.mp4

Understanding the Code with an Analogy

Imagine you are hosting a security system at a bustling event. The yolo.py script acts like a diligent security guard who observes everything around. Just like a guard can use various tools such as a camera or a video feed to spot any inappropriate behavior, the script can use video files, images, or even a live webcam feed to scan for fire and gun incidents. Each command-line argument allows the guard to adjust his tools to the environment, ensuring he can provide the highest level of security depending on the situation at hand.

Exploring Additional Resources

• For the dataset used, check the link: Dataset.
• Learn more about the training process through this Jupyter Notebook.
• Watch the Demo on YouTube.
• Read the research paper: Fire and Gun Violence based Anomaly Detection System Using Deep Neural Networks.

Troubleshooting Tips

If you encounter issues when running the detection script, consider the following troubleshooting suggestions:

• Double-check that all file paths are correctly specified in the command.
• Ensure that required dependencies are installed in your environment.
• If using a webcam, confirm that the device is functional and accessible.
• If the results are not as expected, try running the script with the --verbose option to obtain detailed output and identify issues.

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Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.