TY -的A2 -李,元盟-李,凯盟- Yu Hongliang盟——徐,益盟-罗,小青PY - 2022 DA - 2022/12/06 TI -海洋石油泄漏检测基于猪特征和支持向量机分类器SP - 3296495六世- 2022 AB -石油泄漏事故逐渐增加由于海洋运输和石油加工业的不断发展。监视和管理海洋石油泄漏重要的经济,社会和实际意义在防止海洋石油污染,维护生态平衡。无人机(UAV)已成为一个合适的载体低空石油泄漏检测因其快速部署和低成本。热红外遥感图像作为本研究的研究对象。方法在梯度直方图(猪)特性与支持向量机(SVM)相结合提出了识别海上石油泄漏改善海上低空溢油识别的准确性,实现全天候的监控在近海水域海上石油泄漏。步骤提取猪特征和支持向量机分类的基本原则首先调查。图像预处理,然后进行热红外图像数据收集生产样品。猪样本的特征提取,选择径向基函数作为核函数训练SVM分类器。猪的特征红外图像进行测试计算,然后发送到分类器识别石油泄漏。此外,该方法与反向传播(BP)神经网络方法和局部二值模式(LBP)结合支持向量机分类方法进行分析。 The results show that the oil film recognition method based on the HOG feature and SVM has a recognition accuracy of 91.3% in the environment of small infrared oil film samples, which is significantly better than the BP and LBP-SVM recognition methods, and obtains a shorter training time. The method proposed in this study has obvious advantages in terms of small sample size and processing efficiency, can meet the requirements of all-weather inspection of oil film pollutants by UAV in offshore port areas, and has great application potential in the field of maritime supervision informatization in the future. SN - 1687-725X UR - https://doi.org/10.1155/2022/3296495 DO - 10.1155/2022/3296495 JF - Journal of Sensors PB - Hindawi KW - ER -