Multi-label noisy samples in underwater inspection from the oil and gas industry

Deep learning has shown remarkable success in various machine learning tasks, including multi-label classification. Multi-label classification is a supervised task where an input instance can be associated with multiple labels simultaneously, instead of exclusively one, as in the single-label scenario. When building a multi-label dataset for real-world applications, a recurrent problem is the presence of noisy labels. In this context, noisy labels refer to mislabeled data, which can potentially weaken the performance of supervised models. Although this issue may be well explored for single-label noise, it is still an emerging topic for multi-label applications. In this work, a novel deep learning model that handles multi-label noise is proposed, where we combine the Small Loss Approach Multi-label (SLAM) with a joint loss, in order to automatically identify and rectify noisy labels. The model outperforms in \(2.5\%\) $2.5\%$ for the F1-score state-of-the-art (SOTA) models in the noisy version of the benchmark UcMerced. A new open noisy version of the benchmark TreeSATAI was developed and is now disclosed, where the performance gains reached \(1.8\%\) $1.8\%$ in F-1 Score. Furthermore, the model was able to reduce the presence of noise from \(25\%\) $25\%$ to \(5\%\) $5\%$ in both sets. In addition, we evaluate the model on a real-world application of underwater inspections, to assist with the multi-label classification for an oil and gas company. Our model achieved gains in the F1-Score of \(10\%\) $10\%$ when compared to a standard model (without noise-handling techniques), and up to \(2.7\%\) $2.7\%$ and \(1.9\%\) $1.9\%$ when compared to SOTA models SLAM and JoCoR, respectively.