Trash Detector

What is Trash Detector ?

Trash Detector is an object detection application designed to identify and highlight trash in images. It leverages advanced AI technology to pinpoint waste materials within visual data, making it a useful tool for environmental monitoring, cleanup initiatives, and waste management systems.

Features

• Object Detection: Accurately identifies trash such as bottles, cans, plastics, and other waste materials in images.
• Highlighting:Clearly marks detected trash items for easy visualization and analysis.
• Multi-Image Support: Processes multiple image formats, including JPEG, PNG, and BMP.
• High Accuracy: Utilizes cutting-edge AI models to ensure precise detection.
• Real-Time Processing: Provides quick results for efficient workflow.
• User-Friendly Interface: Intuitive design for ease of use.
• Customizable Settings: Allows users to adjust detection parameters for specific needs.

How to use Trash Detector ?

1. Download and Install: Install the Trash Detector app from the official repository or app store.
2. Upload Images: Load the image(s) you want to analyze into the application.
3. Run Detection: Click the "Detect Trash" button to initiate the scanning process.
4. Review Results: Examine the highlighted trash items in the processed image.
5. Export or Share: Save or share the results for further analysis or reporting.

Frequently Asked Questions

What types of trash can Trash Detector identify?
Trash Detector can identify common waste items such as plastic bottles, cans, paper, cardboard, and other disposable materials. It may not detect irregularly shaped or unidentified objects.

Can I use Trash Detector for real-time video analysis?
Currently, Trash Detector is optimized for image analysis. Real-time video processing is not supported in the basic version, but advanced versions may offer this feature.

How accurate is Trash Detector?
Accuracy depends on the quality of the input image and lighting conditions. In ideal conditions, Trash Detector achieves high accuracy rates, but performance may vary with blurry or poorly lit images.