# Automatic Defect Segmentation on Leather with Deep Learning

arXiv: https://arxiv.org/abs/1903.12139 · HTML: https://mlsanity.com/paper/1903.12139/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2019.

## Built on by (most cited first, 1 of 1)

- [YOLOv5s-GTB: light-weighted and improved YOLOv5s for bridge crack detection](https://mlsanity.com/paper/2206.01498/) (2022)

## Abstract (arXiv)

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.