The Leather Industry Plays A Crucial Role In Fashion, Manufacturing, And Luxury Goods, Where The Quality Of Leather Products Is Of Paramount Importance. Ensuring The Absence Of Defects In Leather Is A Fundamental Aspect Of Maintaining Product Quality. In This Context, We Present A Cutting-edge Approach To Leather Defect Detection Using Deep Learning Techniques.
Our Research Leverages The Power Of Deep Neural Networks, Specifically Convolutional Neural Networks (CNNs), To Develop A Robust And Efficient System For Identifying And Classifying Defects In Leather Hides. We Employ State-of-the-art Image Analysis Methods To Process High-resolution Images Of Leather Surfaces, Enabling The Automated Detection Of Various Types Of Defects, Including Scars, Blemishes, Wrinkles, And Discolorations.
This Paper Outlines The Design, Training, And Evaluation Of The Deep Learning Model, Highlighting Its Ability To Accurately And Rapidly Detect Defects With High Precision And Recall. The Proposed System Holds The Potential To Revolutionize Quality Control Processes Within The Leather Industry, Improving Product Quality, Reducing Waste, And Enhancing Overall Efficiency.
As The Demand For High-quality Leather Products Continues To Grow, Our Work Demonstrates The Potential Of Deep Learning To Advance Quality Control And Defect Detection In This Industry, Ultimately Contributing To The Production Of Flawless Leather Goods.
