A novel data augmentation strategy for gas leak detection and segmentation using Mask R-CNN and bit plane slicing in chemical process environments

Abstract

Gas leak detection is a critical task for environmental and industrial safety, often facilitated through imaging techniques such as Mask R-CNN. However, accurately segmenting gas plumes remains challenging due to their dynamic nature and complex background. In this study, we propose a novel approach to improve gas leak plume segmentation accuracy by combining Mask R-CNN with augmented bit plane images. Initially trained on a dataset of 1000 gas leak images, our model, utilizing a ResNet101 backbone, achieved a commendable F1-Score of 95.6%, outperforming MobileNetV2 and DenseNet169. Through the incorporation of a novel bit plane image augmentation strategy, specifically focusing on the XOR combination of bit planes 4 and 5, the ResNet101 model’s F1-Score significantly improved to 98.7%, showcasing the effectiveness of our approach in enriching the training data and enhancing the model’s ability to generalize to unseen instances. This bit plane augmentation method also demonstrated superior performance compared to other mainstream image enhancement techniques like CLAHE and Gamma correction. These findings suggest promising implications for improving gas leak detection systems, thereby contributing to enhanced safety measures in various industrial and environmental settings, with considerations for real-time industrial deployment.