Help with deep learning for motorbike inspection First of all, I am very new in machine learning and data science, so I am really sorry if my question is completely stupid.

I am doing an internship in machine vision, and people of my office want me to implement a Deep Learning model to inspect a motorbike after being fully assembled. Basically, they want to inspect different parts of the motorbike, and detect if there is a defect or not. However, before doing it in real life, they want to use a miniature of a motorbike in order to study the viability of this project.

This being said, I was thinking about creating one model for each region to be inspected, programing a robot with a camera to take pictures of these regions, and letting the DL models evaluate them. In addition, the DL models would be used for One-Class Classification to detect if the region inspected is OK or Not OK only by analyzing images labeled OK.

However, there are several problems that I will list as follows:

1 – We don’t have (any) data (yes, I told them it is stupid to try DL without any data);

2 – In internet, I could not find dataset of motorbike, much less specifically of the regions to be inspected;

3 – They want to inspect a specific model of motorbike, so they are asking me to do something very very specific, and I suppose that even if I find a dataset of the specific regions of different motorbikes, it will be kind of useless. 

Finally, with all the conditions and problems that I mentioned above, it seems that it is impossible to do what they want, but I would like to ask you about it before giving up, because as I said, I am very new in this subject and I might be wrong. Could you give me your opinion/advice about everything I mentioned here?

Thank you very much!!
Data

If you are falling short of data, you obviously need to create a custom dataset i.e by labelling each part of a motorcycle. If you can collect some images, we can use a pre-trained classifier along with image augmentation technique. You can Google's Dataset Search.

To annotate your dataset, you can find tools here and here. If you need to download a number of images from Google Image Search, try this tool.

Model Architecture

Now, regarding the model, we can use any object localization algorithms to detect various parts of a motorcycle. Next, these individual parts would be fed to different models which will classify the respective faults. For detection, we can use


Faster RCNN and Mask RCNN ( semantic segmentation )
YOLO ( Lower inference time )