多尺度卷积神经网络的图像边缘检测

Edge detecting based on multi-scale convolutional neural network

在受图像拍摄条件、图像内容自身复杂性、图像内容与背景接近程度等多种因素的影响,图像的边缘线检测容易发生漏检、误检。因模型自身设计缺陷或训练样本中边缘像素点与非边缘像素点的不平衡原因,多数算法的图像边缘检测结果普遍存在线条粗、质量较低的问题。提出一种多尺度卷积神经网络模型,由三个分别接受一幅图像的不同尺度输入的子网络结构组成,分别在不同尺度视觉下学习图像的边缘知识。然后按尺度从粗到细对各尺度提取的知识特征进行融合,实现边缘轮廓检测。模型充分利用多尺度技术在图像处理领域的优势,同时引入了自注意力机制以提升卷积特征内部关联性的捕获能力。本文提出了一个新的损失函数,由交叉熵损失函数和L1范数组成,避免训练样本非均衡性对训练模型的影响。使用指标ODS、OIS、AP度量图像边缘检测的质量。在BIPED数据集上测试,三个指标的得分分别为0.845,0.856,0.886。在BSDS500数据集上测试,算法在F-measure指标上得分为0.826。实验结果表明,与其它学习型的算法相比,算法输出图像边缘结果漏检率更低、且质量更高。

Both false detection and missed detection are frequent for most edge detection algorithm, since the picture acquired in bad weather, the complexity of the image content itself, and the edge cues become vague especially when it is close to the background. Due to the design defects of the model or the imbalance between the edge pixels and non-edge pixels in the training samples, the edge detection results of most algorithms generally have the problem of thick lines and low quality. A multi-scale convolutional neural network is proposed, which is composed of three sub-structures and each one accepts one scale of an image. The algorithm learns the knowledge under different scale vision, extracts the edge of the image after the process of fusing gradually the edges from coarse to fine. Except for the advantages of multi-scale technology in image processing, a self-attention mechanism is introduced to improve the ability to capture the internal relevance of convolutional features. A new loss function, which is composed of the cross-entropy loss function and the L1 norm term, is proposed to train the network, and avoid the impact of the imbalance of training samples. Indices: ODS, OIS, AP are used to measure the quality of edge detection. The scores of three indicators are 0.845, 0.856, 0.886 respectively when tested on the BIPED dataset. The algorithm scored 0.826 on the F-measure indicator, tested on BSDS500 dataset. The experimental results show that the algorithm can generate more delicate image edge results.