  Leather is a natural and durable material created through a process of
tanning of hides and skins of animals. The price of the leather is subjective
as it is highly sensitive to its quality and surface defects condition. In the
literature, there are very few works investigating on the defects detection for
leather using automatic image processing techniques. The manual defect
inspection process is essential in an leather production industry to control
the quality of the finished products. However, it is tedious, as it is labour
intensive, time consuming, causes eye fatigue and often prone to human error.
In this paper, a fully automatic defect detection and marking system on a calf
leather is proposed. The proposed system consists of a piece of leather, LED
light, high resolution camera and a robot arm. Succinctly, a machine vision
method is presented to identify the position of the defects on the leather
using a deep learning architecture. Then, a series of processes are conducted
to predict the defect instances, including elicitation of the leather images
with a robot arm, train and test the images using a deep learning architecture
and determination of the boundary of the defects using mathematical derivation
of the geometry. Note that, all the processes do not involve human
intervention, except for the defect ground truths construction stage. The
proposed algorithm is capable to exhibit 91.5% segmentation accuracy on the
train data and 70.35% on the test data. We also report confusion matrix,
F1-score, precision and specificity, sensitivity performance metrics to further
verify the effectiveness of the proposed approach.
