  In a factory production line, different industry parts need to be quickly
differentiated and sorted for further process. Parts can be of different colors
and shapes. It is tedious for humans to differentiate and sort these objects in
appropriate categories. Automating this process would save more time and cost.
In the automation process, choosing an appropriate model to detect and classify
different objects based on specific features is more challenging. In this
paper, three different neural network models are compared to the object sorting
system. They are namely CNN, Fast R-CNN, and Faster R-CNN. These models are
tested, and their performance is analyzed. Moreover, for the object sorting
system, an Arduino-controlled 5 DoF (degree of freedom) robot arm is programmed
to grab and drop symmetrical objects to the targeted zone. Objects are
categorized into classes based on color, defective and non-defective objects.
