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Separating and then recycling of waste materials is necessary for every society. The segregation and recycling processes that are currently in use require facilities to separate garbage by hand and use a series of large filters to separate out more defined objects. The goal is to find an automatic method for sorting waste from bins. This helps in processing plants more efficiently and helps to reduce waste, its a known fact that the employees sort everything with 100% accuracy. This will have positive environmental effects also benefits economic effects.
According to nature, everything is degraded and reused, which means there is no waste. When plants and animals die they get rot into the ground and they provide nutrients for other living plants and animals. For a long time, both the reality of the waste and definition of waste have been traveling together. There are many types of definitions for waste, but they have common semantic relations. Almost in human history, every highly-populated area developed their design to dispose of the waste generated by humans.
Also, the wastes generated by humans was often confused with staying fit and hygiene problems. Previously, in ancient middle age periods, there was a rapid increase in population and industries which resulted in increased solid wastes in the streets. To prevent the spreading of the disease, the departments decided to move the wastes from the street. Actually, this was considered as the first garbage collection attempt.
Why: Problem statement
For a sustainable economy, Waste management and recycling is considered as a basic part. For best and safe recycling and management, it is needful to make use of intelligent automated systems instead of involving humans as workers in the waste management system. This is considered as one of the early works explaining the benefits of the current intelligent approaches and designs. In this project, we have explained on well-known deep convolutional neural network architectures.
Using the built model, we will be able to improve the waste management. This will help all urban and rural cities to become more resistant to extreme climate conditions such as flooding, damaged infrastructure, and their livelihoods.
How: Solution description
Step 1: The collected wastes are divided using CNN ( using convolutional neural network )
CNN a Deep Learning algorithm which can take in an input image, assign importance (learnable weights and biases) to various aspects/objects in the image and be able to differentiate one from the other. The pre-processing required in a ConvNet is much lower as compared to other classification algorithms. While in primitive methods, filters are hand-engineered, with enough training, ConvNets have the ability to learn these filters/characteristics.
Step 2: The collected data is divided into train data (85%) and Test data (15%)
Training Data - 22564 Images
Test Data - 2513 Images
Step 3: The parameters used are Epochs (4) and Batch size(706)
Step 4: The waste is categorized into organic and recycle waste. Then the percentage of rate is analyzed. Investments and profits are calculated.
How is it different from competition
Disposal of waste has become a significant problem all over the world. Even though the production of waste keeps on increasing, the recycle and reuse of this waste have not improved. When we analyzed the drawbacks of the recycling process the significant causes were the separation of the waste into organic and recycle waste and the money investment for this process.
Another disadvantage of existing models is that they are a little bit slower in prediction time. To make it faster the prediction performance of the models was altered. The connection patterns of the skip connections inside dense blocks were also altered. Our project recycle net is a carefully optimized deep convolutional neural network architecture for separations of selected recyclable object classes. The differences between the existing and proposed model are explained in the table below: