Overview / Usage

Trash detection is the process of identifying and classifying different types of trash, such as plastics, metals, and paper, using machine learning techniques. This technology is becoming increasingly important as waste management is becoming a major environmental issue worldwide. The process of trash detection usually involves capturing images of the waste using cameras or sensors, which are then processed by machine learning algorithms to identify the type of waste present. The algorithms can use a variety of techniques, such as computer vision, pattern recognition, and deep learning, to accurately classify the trash. The main application of trash detection technology is in waste management, where it can be used to sort different types of waste, making recycling more efficient and reducing the amount of waste that ends up in landfills or oceans. It can also be used in public spaces, such as parks and beaches, to detect and monitor litter, helping to keep these areas clean. Trash detection has a number of potential benefits, including reducing waste, conserving resources, and reducing pollution. It can also help to create new economic opportunities, such as in recycling industries, and improve public health by reducing exposure to hazardous materials. Overall, trash detection technology has the potential to be a powerful tool in the fight against environmental pollution and waste management.

Methodology / Approach

Trash detection is a process of detecting and classifying different types of waste materials using machine learning algorithms. This technology has become increasingly important in recent years, as the world faces growing environmental issues caused by waste production and disposal.

The methodology for trash detection typically involves several steps, including image capture, data pre-processing, feature extraction, and classification. Each of these steps is important in order to ensure accurate and reliable results.

The first step in the methodology for trash detection is image capture. Images of waste materials can be captured using cameras, sensors, or other devices. The images should be taken in a controlled environment with consistent lighting and angles to ensure that the images are of high quality and that the waste materials can be easily identified.

Once the images have been captured, the next step is data pre-processing. This involves cleaning and preparing the data for analysis by removing any noise, artfacts, or irrelevant information. The data is then scaled and normalized to ensure that it is consistent and comparable across different images.

The third step in the methodology is feature extraction. This involves identifying the important features or characteristics of the waste materials that can be used to distinguish them from one another. The features can include color, texture, shape, and size, among others. Machine learning algorithms are then used to extract these features from the images.

Once the features have been extracted, the next step is classification. This involves using machine learning algorithms to classify the waste materials into different categories, such as plastics, metals, or paper. The algorithms can use a variety of techniques, such as support vector machines, decision trees, or deep learning, to accurately classify the waste materials.

To improve the accuracy of the classification, it is important to use a large and diverse dataset of waste materials for training the machine learning algorithms. The dataset should include images of different types of waste materials, captured in different environments and under different conditions.

In addition to these steps, it is also important to evaluate the performance of the trash detection system using metrics such as accuracy, precision, recall, and F1 score. This can help to identify areas where the system may need improvement and to optimize the system for better performance. And we use python frameworks like flask and streamlit etc to make it as web application and using this to alert the cooperation to clean the trash.

Overall, the methodology for trash detection involves several important steps, including image capture, data pre-processing, feature extraction, and classification. By using machine learning algorithms to accurately identify and classify different types of waste materials, this technology has the potential to play an important role in waste management and environmental conservation efforts.

Technologies Used

• INTEL ONEAPI
• PANDAS
• SCIKIT LEARN
• TENSORFLOW
• KERAS
• RESNET152
• DENSENET169
• CNN
• FLASK

Repository

https://github.com/Gokulprasanth-t/TRASHEYE_IntelOneAPI.git