Ethylene leakage detection has become one of the most important research directions in the field of target detection due to the fact that ethylene leakage in the petrochemical industry is closely related to production safety and environmental pollution. Under infrared conditions, there are many factors that affect the texture characteristics of ethylene, such as ethylene concentration, background, and so on. We find that the detection criteria used in infrared imaging ethylene leakage detection research cannot fully reflect real-world production conditions, which is not conducive to evaluate the performance of current image-based target detection methods. Therefore, we create a new infrared image dataset of ethylene leakage with different concentrations and backgrounds, including 54275 images. We use the proposed dataset benchmark to evaluate seven advanced image-based target detection algorithms. Experimental results demonstrate the performance and limitations of existing algorithms, and the dataset benchmark has good versatility and effectiveness.

翻译：乙烯泄漏检测已成为目标检测领域最重要的研究方向之一，因为石油化工行业的乙烯泄漏与生产安全和环境污染密切相关。在红外条件下，乙烯浓度、背景等多种因素会影响乙烯的纹理特征。我们发现，当前红外成像乙烯泄漏检测研究中所使用的检测标准无法充分反映真实生产环境，这不利于评估现有基于图像的目标检测方法的性能。为此，我们创建了一个包含不同浓度和背景的乙烯泄漏红外图像数据集，共计54275张图像。利用该基准数据集，我们评估了七种先进的基于图像的目标检测算法。实验结果表明了现有算法的性能与局限性，且该基准数据集具有良好的通用性和有效性。