Efficient Smoke Segmentation Using Multiscale Convolutions and Multiview Attention Mechanisms

Efficient segmentation of smoke plumes is crucial for environmental monitoring and industrial safety. Existing models often face high computational demands and limited adaptability to diverse smoke appearances. To address these issues, we propose SmokeNet, a deep learning architecture integrating multiscale convolutions, multiview linear attention, and layer-specific loss functions. Specifically, multiscale convolutions capture diverse smoke shapes by employing varying kernel sizes optimized for different plume orientations. Subsequently, multiview linear attention emphasizes spatial and channel-wise features relevant to smoke segmentation tasks. Additionally, layer-specific loss functions promote consistent feature refinement across network layers, facilitating accurate and robust segmentation. SmokeNet achieves a segmentation accuracy of 72.74% mean Intersection over Union (mIoU) on our newly introduced quarry blast smoke dataset and maintains comparable performance on three benchmark smoke datasets, reaching up to 76.45% mIoU on the Smoke100k dataset. With a computational complexity of only 0.34 M parameters and 0.07 Giga Floating Point Operations (GFLOPs), SmokeNet is suitable for real-time applications. Evaluations conducted across these datasets demonstrate SmokeNet’s effectiveness and versatility in handling complex real-world scenarios.