基于卷积神经网络的小型建筑物检测算法

针对基于传统卷积神经网络的建筑物目标检测算法对于小型建筑物检测准确率低的问题, 提出一种基于Mask-区域卷积神经网络(Mask-region convoluional neural networks, Mask-RCNN)模型的小目标检测算法模型。该模型对Mask-RCNN模型中的特征提取网络进行了改进, 设计了一种带有注意力机制的多尺度组卷积神经网络, 有效解决了小目标有用特征较少且易被背景特征和噪声干扰的问题。航拍图像实验结果表明, 改进的检测模型使小型建筑物目标检测准确率较原始Mask-RCNN模型提升了28.9%, 达到了0.663。并且整体检测准确率达到了0.843, 有效提升了航拍建筑物检测准确性。

Small building detection algorithm based on convolutional neural network

Aiming at the low accuracy of building target detection algorithm based on traditional convolutional neural network for small buildings, a small target detection algorithm model based on Mask-region convolutional neural networks (Mask-RCNN) model is proposed. The model improves the feature extraction network in Mask-RCNN model, and designs a multi-scale group convolution neural network with attention mechanism, which effectively solves the problem that small targets have few useful features and are easy to be disturbed by background features and noise. The experimental results of aerial images show that the improved detection model improves the target detection accuracy of small buildings by 28.9% compared with the original Mask-RCNN model, reaching 0.663. And the overall detection accuracy reaches 0.843, which effectively improves the accuracy of aerial building detection.