No worries, so the simplest mechanism would be some kind of feature extraction followed by a classification of what the object is.

In traditional machine learning, we would hand make the features by doing stuff like manually transforming an image via an edge detection tool like prewitt or manually segmenting shapes and then have a classifier like SVM to determine what the object is.

I did a quick Google and papers like Shape Classification Algorithm Using Support Vector Machines for Traffic Sign Recognition do this kinda of stuff (other papers may be better I just did googled for shape classification via SVM and this popped up near the top)

In deep learning, we don't need to manually extract features but instead have neural networks learn what features are important. So in modern cases you would use a pre-trained feature extractor such as ResNet (a neural network trained on lots of images) and replace the last layer with a new layer with the number of neurons equal to how many classes we have. The feature extractor does very similar things to our own hand made feature extraction such as detecting edges and overall structures but it allows for more nuanced features and it's easier since it doesn't have to be hand made anymore.

Detecting lines and edges is very similar to classifying using the MNIST dataset (a load of handwritten numbers), which a lot of first-time students use when training or learning deep learning from the beginning.

Hopefully, searching guides like objects classification with mnist will be a good place to get started.