SmartBin: Teaching Trash Cans to Think with Deep Learning

Waste management is one of the most persistent challenges in modern urban life. As cities grow, so do our mountains of trash. The traditional way to deal with this—manual collection and sorting—is labor-intensive, costly, unhygienic, and often inefficient.

What if we could bring intelligence to the very first step of waste management: the bin itself?

A group of researchers set out to answer that question with SmartBin, a low-cost hardware solution that uses deep learning to automatically segregate garbage at the source. In their paper, “A deep learning approach based hardware solution to categorise garbage in environment”, they present a prototype that can distinguish between biodegradable and non-biodegradable waste, and physically sort it into separate compartments.

This article breaks down their approach, looking at the hardware they used, the deep learning models they tested, and the real-world performance of SmartBin. You’ll see how a Raspberry Pi, a camera, and a powerful neural network can team up to create a practical solution for a complex environmental problem.

The Challenge: A Diverse and Messy Problem

Automating garbage sorting is far from straightforward. Unlike typical object detection tasks—finding cats or cars—garbage is visually diverse. It can be any shape, size, color, or texture. An apple core, a crumpled newspaper, and a plastic bottle share almost no visual characteristics.

Researchers have tried various approaches to tackle this:

• Mobile apps like SpotGarbage use Convolutional Neural Networks (CNNs) to identify trash piles in user-submitted photos.
• IoT cameras and sensors monitor bin fill levels.
• Robotic arms and advanced computer vision sort items on conveyor belts.

The authors of this study aimed for a solution that is both effective and accessible. They combined image classification with simple, off-the-shelf IoT components, creating a system that can be implemented inside a trash bin—sorting waste the moment it’s thrown in.

Building the SmartBin: Dataset, Hardware, and Workflow

The Dataset: Teaching AI to Recognize Trash

The researchers compiled a dataset of 9,516 images from three public sources, including the popular TrashNet repository. Images were divided into seven classes:

• Organic waste
• Cardboard
• Paper
• Metal
• Glass
• Plastic
• Other trash

These seven classes were grouped into two categories for SmartBin’s sorting mechanism:

• Biodegradable: Organic waste, paper, cardboard
• Non-biodegradable: Metal, glass, plastic, other trash

Biodegradable items made up 51.76%, while non-biodegradable made up 48.24%.

A treemap provides a clear visual of the dataset distribution.

Sample images from each category were captured in high resolution against a clean, white background to minimize noise.

System Design and Workflow

SmartBin’s hardware prototype integrates sensing, processing, and mechanical sorting. A Raspberry Pi orchestrates the workflow.

How it works:

1. Activation: User presses the “USE ME” button. The lid opens, and subsystems wake up.
2. Detection: IR sensor emits waves. Trash on the central separator disk obstructs them.
3. Image capture: IR receiver detects reflection, triggers Pi Camera after a 5-second delay (to settle object & adjust focus/lighting).
4. Classification: Image fed to a pre-trained CNN on Raspberry Pi.
5. Decision: CNN predicts one of six garbage classes; result mapped to biodegradable (1) or non-biodegradable (0).
6. Segregation: Servo motor tilts disk to drop garbage into the correct compartment, returns to neutral position.

A hardware interrupt switch can safely end the infinite loop script.

Hardware Components

SmartBin uses affordable, widely available parts, with a street-level version costing about ₹4050 (~$50 USD).

Key components (see circuit diagram below):

• Raspberry Pi 3B: Runs OS, driver script, and deep learning model.
• Pi Camera: 5MP module connected via CSI port for image capture.
• IR Sensor: Detects presence and acts as camera trigger.
• Servo Motor: Tilts separator disk for sorting.

Physical prototype:

The Brains of SmartBin: CNN Face-Off

With the hardware ready, the team tested four well-known pre-trained CNN architectures using transfer learning—retraining only the final layer to classify their six garbage categories.

1. AlexNet

Winner of ImageNet 2012; 5 convolutional + 3 fully connected layers. Introduced ReLU and dropout.

2. VGG-16

Uniform stacks of 3×3 convolutions; simple but heavy (138M parameters).

3. ResNet

Skip connections enable deeper networks by avoiding vanishing gradients.

4. InceptionNet V3

Processes inputs through multiple filter sizes in parallel; factorizes convolutions for efficiency. Optimized for both depth and width, ideal for devices like Raspberry Pi.

Building blocks include:

• Stem: Initial downsampling
• Inception Modules A, B, C: Multi-scale feature extraction
• Reduction Modules A, B: Dimensionality reduction with channel expansion

Results: The Winner

Training/validation accuracy & loss comparisons:

Loss & accuracy curves:

Highlights:

• VGG-16: Overfit; large gap between train & validation accuracy (98.76% vs 87.52%).
• AlexNet: High validation accuracy (97.95%).
• ResNet: Solid performer at 97.21% validation accuracy.
• InceptionNet V3: 96.23% validation accuracy and lowest validation loss (0.13).

Prediction speed mattered too:

InceptionNet V3 was fastest while maintaining strong performance—making it the chosen SmartBin brain.

Real-World Testing

In practice, garbage isn’t photographed against neat white backgrounds.

First test: Crushed cardboard ball → misclassified as non-biodegradable due to poor lighting, cluttered background, & blur.

Fixes:

1. Added a plain white sheet inside bin to reduce background noise.
2. Increased ISO for low-light performance & ensured 5s delay for focused images.

This improved classification for various objects: metallic pen, newspaper, plastic wrapper, potato, glass jar.

Failure case: cotton earbud misclassified as biodegradable due to insufficient similar data in training.

This illustrates the need for more diverse, edge-case training data.

Comparison with Other Solutions

SmartBin’s unique strength is physical segregation in hardware. Many prior solutions are software-only.

SmartBin achieved 96.23–98.15% accuracy and real-time actuation within the bin.

Conclusion & Future Directions

SmartBin demonstrates how deep learning + IoT hardware can tackle environmental problems. By pairing an efficient model like InceptionNet V3 with simple mechanical sorting, the team automated waste segregation at the source.

Limitations:

• Handles one item at a time.
• Sensitive to image quality.

Future Work:

• Expand dataset: Capture diverse images covering more edge cases.
• Optimize speed: Use faster cameras or reduce delay times.
• Enhance features: Classify more waste subcategories, sort multiple items simultaneously, integrate bin-fill sensors.

Projects like SmartBin give us a glimpse of a future where technology helps cities function more sustainably—bringing intelligence to the humble trash can for cleaner, more efficient waste management.