System and method for detection of mobile device fault conditions

A method comprising:

• installing an executable program on a mobile device;
• prompting a user of the mobile device to place the mobile device so that a display section of the mobile device faces a reflective surface of a mirror;
• performing a tracking calibration function to adjust the mobile device position and display for optimal image capture;
• modifying a brightness setting and a camera sensitivity setting of the mobile device to optimize image capture;
• and
• determining that the mobile device is in an acceptable orientation with respect to the mirror, and thereupon:
• capturing an image of the mobile device as reflected from the mirror;
• processing the captured image to determine whether a defect can be identified in the display of the mobile device, wherein the processing further comprises:
• extracting, from the captured image, a subimage corresponding to a screen of the mobile device;
• and
• transforming a perspective of the subimage into a rectangular aspect;
• presenting transformed subimage to a pre-trained convolutional neural network (CNN) to identify one or more device defects;
• and
• obtaining from the pre-trained neural network an indication of a first defect from the one or more device defects that corresponds to the screen of the mobile device, further including an indicia, created through class activation mapping functions, showing a portion of the mobile device screen that activates a defective class in the pre-trained neural network.