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Final-year project: Poop Patrol - AI detection of dog fouling

Project Title

Poop Patrol: AI detection of dog fouling

Programme / year

TU821/4

Objective

To design, implement and test a machine vision system to automatically detect dog fouling on pavements and other surfaces using a camera.

Project Description

Dog fouling is a nuisance for most pedestrians, requiring vigilance while walking, and occasionally leading to the unpleasant job of cleaning a dirty shoe or the wheel of a buggy. However, it's a more serious problem for wheelchair users, visually impaired pedestrians and young children who are at increased risk of serious illness caused by exposure to pathogens in dog faeces.

The objective of this project is to design, implement and test a machine vision system that detects dog fouling in images. Possible applications include:

• Autonomous pavement cleaning robots,
• Real-time audio warning system for visually impaired pedestrians,
• Automated data gathering for local authorities.

The system will be based on a neural network, possibly implemented using the PyTorch framework. An important element of this project is the creation of a suitable dataset of images, including a large number that contain dog faeces and a large number that don't. All images must be labelled by a human observer, so that the dataset can be used to train, test and validate the neural network. The creation of the dataset can be carried out directly by the student. Alternatively, some form of crowd-sourcing might be used to create a larger set of images.

Key Outcomes

1. A large labelled dataset of pavement images, including examples that contain dog faeces and others that do not.
2. A trained neural network classifier that recognises images that contain dog faeces.
3. The trained classifier may optionally be deployed in an example application, but this is not a requirement.

Additional project information

This project will involve:

1. Data gathering - probably taking many pavement photographs in real-life settings and manually labelling them.
2. Programming (e.g. Python, PyTorch or similar).

Is a particular major required?

No major option is required for this project.