Pothole Detection, without wrecking a wheel.

Is it or isn't it?

 

I decided to build and train a pothole image detector as part of following along with Fast Ai's Practical Deep Learning course. The reason I chose the pothole detector was that at a previous company a similar thing had been built from scratch, completely training it ourselves. Potholes are also something I come across very regularly on the British roads I cycle around. Having come off my bicycle once in the last year I am not in a hurry to repeat the experience.


The thing that immediately struck me was how quickly the model was trained and how easy it was to do. This is mainly because what we are doing is fine tuning an existing image model rather than trying to train one up from nothing, the base model already knows a lot about images before we start. The project where we trained the pothole detector from scratch took at least five weeks to get to something reliable, whereas I have trained something that seems to work in about five minutes. It's not quite a fair comparison as there was always also a need to slice videos into images and extract metadata from them, so there was more scope in the original project.


Fast AI is designed to make the development and training of models quick and easy. It wraps a lot of the complexities of the underlying libraries, and provides utilities that allow you to download images from an internet search. There are data types that are designed to provide categories and labels, test and training data for the model you are training or tuning. I am sure that there is much more in the Fast AI library that I haven't got to yet.


The other big difference between the earlier project, which was several years ago, is that we are able to use free compute and deployment resources to evaluate and tune the model. The earlier project used the boss’s video gaming machine because that had some GPUs in it and he went through Call of Duty withdrawal whilst training was done.


The specific tools we used were Jupyter Notebooks hosted by Kaggle, a frontend generated by Gradio and hosted at Hugging Face and the code basically came from chapter 1 and 2 of the Fast AI course. The end result of that is a public demonstration that you can see here:




So how did it do? I have only tried it out on one or two images downloaded from the internet and it performs well on those, but they might well be in the training set. I have tried it on one photograph of a pothole that I took and it got that wrong, possibly because it was more of a pot crack than a pothole. I'm planning to take more photographs and carry out more of a formal evaluation of how the model is doing, and maybe tune it again on images that are more relevant to the roads that I cycle along. Fast AI seems to provide some tools and utilities to do this, so look out for another exciting instalment.

Chat vs Search

 I had to add a couple of features to the UI on the MIR infrastructure router. Not really being a UI person I could either look them up, or I could ask ChatGPT. In the end I did both, how did it pan out?

Colour Picker

The first thing I wanted to add was a colour picker. MIR now has a lot of data layers for the UK and it is helpful to be able set the colours for these, the plan was to trigger a colour picker by clicking on a randomly coloured block that the layer has as a default.

I had a few goes at this but didn't get anything satisfactory here's an example below:

system_message = "You are an expert HTML, Javascipt and CSS coder." user_message = """ Create a colour picker to change the colour of an element in a web page. """

In some ways this gave me what I asked for, HTML, CSS and JS that would, probably, change the colour of an element. Unfortunately the colour picker is an HTML form field that you have to type a CSS RGB string into rather than an actual picker.

In other attempts I managed to get a basic JS colour picker working that consisted of three sliders that would change the RGB  parameters of a colour, again setting the colour of the element when you click the button. It potentially works but isn't very interactive.

This still wasn't ideal, so I tried a Google search for a JavaScript colour picker which led me to a site with a

collection of these and out of those I chose one called Swatchy.  This is where search truly wins over chat,

because you can actually see the colour pickers in action and choose one with functionality that you like. 

Modal

The other thing I wanted to add was a help modal, things have got a bit more complicated and help where you need it seemed to be a good idea.

Again I tried ChatGPT first these are the prompts that I came up with :

system_message = "You are an expert HTML, Bootstrap 5,Javascript and CSS coder."

user_message = """

Create a box to display some help text. It should open when a button with a help icon is clicked and close when a close button is clicked.

"""

This produced what looked like workable HTML, JavaScript and CSS to create a popup box, which I guess is what one would expect, but as I am using Bootstrap I don't need.

Altering the text to ‘create a modal’ did better, in that it just produced HTML and CSS to hide and show the box. It did also make use  of the ‘data-bs-dismiss’ Bootstrap attribute to dismiss  the modal, however the CSS was unnecessary as this is already included in Bootstrap.

 A quick search for ‘bootstrap 5 modal’ took me to the Bootstrap documentation and gave me a small amount of code that I could cut and paste into my site.  2-0 to search.

TL;DR

Search isn't dead yet, especially for anything visual. Search can give you options, writing good prompts is hard.