The demand for real-time identification of oil spills in disaster emergency response is urgent, Unmanned Aerial Vehicles (UAVs) are important monitoring means for oil spills by advantage of their flexible, fast and low-cost, so it's crucial of developing lightweight model for UAVs. This paper proposed a lightweight hyperspectral identification model called SR-SqueezeNet, which based on SqueezeNet model and used the designed smooth-type activation function Smooth-ReLU. And this research conducted a series of experiments based on the multi-dimensional airborne images of the oil spills. The results show that SR-SqueezeNet performs the best in both model lightweighting and extraction accuracy. Compared with the traditional SqueezeNet, the identification accuracy is improved by 1.92 %, the number of parameters is reduced by 75.11 %, and the model size is reduced from 26.46 MB to 12.15 MB. Therefore, the SR-SqueezeNet model has potential ability in the practical needs of oil spill UAVs' lightweight detection.

中文翻译：

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SR-SqueezeNet：基于平滑激活函数的轻量级溢油高光谱识别模型


灾害应急响应中对溢油实时识别的需求迫切，无人机（UAV）凭借其灵活、快速和低成本等优势成为重要的溢油监测手段，因此开发无人机轻量化模型至关重要。本文提出了一种名为 SR-SqueezeNet 的轻量级高光谱识别模型，该模型基于 SqueezeNet 模型，并使用了设计的平滑型激活函数 Smooth-ReLU。而这项研究基于漏油的多维航空图像进行了一系列实验。结果表明，SR-SqueezeNet 在模型轻量化和提取精度方面表现最佳。与传统 SqueezeNet 相比，识别精度提高了 1.92 %，参数数量减少了 75.11 %，模型大小从 26.46 MB 减小到 12.15 MB。因此，SR-SqueezeNet 模型在溢油无人机轻量化探测的实际需求中具有潜在能力。