SmokeNet：利用多尺度卷积和多视图注意力机制实现高效烟雾分割
SmokeNet: Efficient Smoke Segmentation Leveraging Multiscale Convolutions and Multiview Attention Mechanisms
AI总结：
针对烟雾分割模型计算成本高、外观适应性差的问题，本文提出融合多尺度卷积、多视图线性注意力和分层损失的 SmokeNet，并在四个数据集上验证其效率与精度。
摘要：
烟羽的高效分割对于环境监测和工业安全至关重要，能够检测并缓解采石场爆破和野火等活动产生的有害排放。准确的分割有助于环境影响评估、及时干预以及符合安全标准。然而，现有模型通常面临计算需求高以及对多样烟雾外观适应性有限的问题，限制了其在资源受限环境中的部署。为解决这些问题，我们提出了 SmokeNet，这是一种新颖的深度学习架构，利用多尺度卷积和多视图线性注意力机制，并结合特定层损失函数，以处理多样烟羽的复杂动态，确保在各种环境中实现高效且准确的分割。此外，我们使用四个数据集评估了 SmokeNet 的性能和多功能性，其中包括我们向社区公开的采石场爆破烟雾数据集。结果表明，SmokeNet 在计算效率和分割准确性之间保持了良好的平衡，使其适合部署在环境监测和安全管理系统中。通过贡献一个新数据集并提供高效的分割模型，SmokeNet 提升了在多样且具有挑战性的环境中的烟雾分割能力。
英文摘要：
Efficient segmentation of smoke plumes is crucial for environmental monitoring and industrial safety, enabling the detection and mitigation of harmful emissions from activities like quarry blasts and wildfires. Accurate segmentation facilitates environmental impact assessments, timely interventions, and compliance with safety standards. However, existing models often face high computational demands and limited adaptability to diverse smoke appearances, restricting their deployment in resource-constrained environments. To address these issues, we introduce SmokeNet, a novel deep learning architecture that leverages multiscale convolutions and multiview linear attention mechanisms combined with layer-specific loss functions to handle the complex dynamics of diverse smoke plumes, ensuring efficient and accurate segmentation across varied environments. Additionally, we evaluate SmokeNet's performance and versatility using four datasets, including our quarry blast smoke dataset made available to the community. The results demonstrate that SmokeNet maintains a favorable balance between computational efficiency and segmentation accuracy, making it suitable for deployment in environmental monitoring and safety management systems. By contributing a new dataset and offering an efficient segmentation model, SmokeNet advances smoke segmentation capabilities in diverse and challenging environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
