  Modern industry requires modern solutions for monitoring the automatic
production of goods. Smart monitoring of the functionality of the mechanical
parts of technology systems or machines is mandatory for a fully automatic
production process. Although Deep Learning has been advancing, allowing for
real-time object detection and other tasks, little has been investigated about
the effectiveness of specially designed Convolutional Neural Networks for
defect detection and industrial object recognition. In the particular study, we
employed six publically available industrial-related datasets containing defect
materials and industrial tools or engine parts, aiming to develop a specialized
model for pattern recognition. Motivated by the recent success of the Virtual
Geometry Group (VGG) network, we propose a modified version of it, called
Multipath VGG19, which allows for more local and global feature extraction,
while the extra features are fused via concatenation. The experiments verified
the effectiveness of MVGG19 over the traditional VGG19. Specifically, top
classification performance was achieved in five of the six image datasets,
while the average classification improvement was 6.95%.
