基于深度卷积神经网络的油罐目标检测研究

油罐是用于储存油品的工业设施，常用在炼油厂等工业园中，通过卫星或航空遥感图像实现油罐目标的快速检测，可以实现对侵占生态保护红线的疑似工业园区的快速查找，为自然资源监管和生态环境保护提供科学技术支持。探讨了基于深度卷积神经网络在高分辨率遥感影像目标检测中的有效性，基于深度学习目标检测算法中具有代表性的Faster R-CNN（Convolutional Neural Network）和R-FCN（Region-based Fully Convolutional Network）框架，通过对ZF、VGG16、ResNet-50 3种网络模型进行训练和测试，实现了遥感影像上油罐目标的快速检测；通过修改锚点尺度和数量，丰富了候选框类型和数量，提升了油罐的目标检测精度，最优召回率接近80%。研究表明：深度卷积神经网络能够实现对高分辨率遥感影像中油罐目标的快速检测，为深度学习技术在遥感小目标的快速检测提供了实例和新的思路。

Oil Tank Detection from Remote Sensing Images based on Deep Convolutional Neural Network

Oil tanks are industrial facilities for storing oil products, which are commonly used in industrial parks such as oil refineries. The rapid detection of oil tank target through satellite or aerial remote sensing images can quickly find suspected industrial parks, providing scientific and technical support for natural resource regulation and ecological environment protection. This paper discussed the possibility of object detection with high-resolution remote sensing images based on deep convolutional neural network. The state-of-the-art algorithms of Faster R-CNN (Convolutional Neural Network) and R-FCN (Region-based Fully Convolutional Network) and three network models were applied for oil tank detection from high-resolution remote sensing images. To promote the detection accuracy and execution efficiency for the oil tank target, an improved approach by increasing the scales of the anchor was proposed. The optimum recall reached about 80%. The results confirm that deep learning network approach can rapid detect oil tank from high-resolution remote sensing image. This provide an example and new idea for rapid detection small target from remote sensing image by deep learning technology.