Towards Robust Ferrous Scrap Material Classification with Deep Learning and Conformal Prediction
CoRR(2024)
Abstract
In the steel production domain, recycling ferrous scrap is essential for
environmental and economic sustainability, as it reduces both energy
consumption and greenhouse gas emissions. However, the classification of scrap
materials poses a significant challenge, requiring advancements in automation
technology. Additionally, building trust among human operators is a major
obstacle. Traditional approaches often fail to quantify uncertainty and lack
clarity in model decision-making, which complicates acceptance. In this
article, we describe how conformal prediction can be employed to quantify
uncertainty and add robustness in scrap classification. We have adapted the
Split Conformal Prediction technique to seamlessly integrate with
state-of-the-art computer vision models, such as the Vision Transformer (ViT),
Swin Transformer, and ResNet-50, while also incorporating Explainable
Artificial Intelligence (XAI) methods. We evaluate the approach using a
comprehensive dataset of 8147 images spanning nine ferrous scrap classes. The
application of the Split Conformal Prediction method allowed for the
quantification of each model's uncertainties, which enhanced the understanding
of predictions and increased the reliability of the results. Specifically, the
Swin Transformer model demonstrated more reliable outcomes than the others, as
evidenced by its smaller average size of prediction sets and achieving an
average classification accuracy exceeding 95
method proved highly effective in clarifying visual features, significantly
enhancing the explainability of the classification decisions.
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