The project will be focusing on object-detection models and classification models. These models can be trained to detect issues in an image and can be used to detect faults, search for the presence or absence of something in an image. This device will be equipped with wireless communication technology so that it can link up with an accompanying app or the internet. The app will be able to communicate with the device, and the user of the app will be able to see an overview of all the detections the device made and judge the reliability of the device. The device will also be able to connect to the internet, this is in the situation it is unable to run the models due to specific reasons, the device will then connect to a server and run the test externally. Then the project will examine different methods of training object-detection and classification models to find the best method suitable for a low computation device such as the raspberry pi. For many of the use-cases that this device can be used for it will need to focus on both speed and portability so the examination of model training will be a very important part of this project. The most likely solution that the project will follow is writing training algorithms in python using modules such as pytorch or tensorflow but the potential of these models will still need to be examined. The device will have many different use-cases such as assembly line quality testing. The device can be set up in a stationary position along an assembly line and detect objects passing it. There are many different use-cases for this device as it can be set up in any location and it can be trained to run a huge variety of different models.