| 一种基于DexiNed改进的红外图像边缘检测算法 |
| |
| 引用本文： | 何谦,刘伯运.一种基于DexiNed改进的红外图像边缘检测算法[J].红外技术,2021,43(9):876-884. |
| |
| 作者姓名： | 何谦 刘伯运 |
| |
| 作者单位： | 海军工程大学动力工程学院, 湖北 武汉 430033 |
| |
| 摘 要： | 相对于可见光图像边缘检测，目前针对红外图像边缘检测的研究较少，且大多基于传统方法，如边缘检测算子、数学形态学等，其本质上都是只考虑红外图像局部的急剧变化来检测边缘，因而始终受限于低层次特征。本文提出了一种基于深度学习的红外图像边缘检测算法，在DexiNed（Dense Extreme Inception Network for Edge Detection）的基础上，缩减了网络规模，并在损失函数中引入了图像级的差异，精心设置了损失函数的参数，进而优化了网络性能。此外，还通过调整自然图像边缘检测数据集来近似模拟红外图像边缘检测数据集，对改进后的模型进行训练，进一步提高了网络对红外图像中边缘信息的提取能力。定性评价结果表明，本文方法提取的红外图像边缘定位准确、层次清晰、细节丰富、贴合人眼视觉，使用了SSIM（Structural Similarity Index Measure）和FSIM（Feature Similarity Index Measure）指标的定量评价结果进一步体现了本文方法相比于其他方法的优越性。
|
| 关 键 词： | 红外图像 边缘检测 深度学习 |
| 收稿时间： | 2021-03-23 |
Improved Infrared Image Edge Detection Algorithm Based on DexiNed |
| |
| Affiliation: | College of Power Engineering, Naval University of Engineering, Wuhan 430033, China |
| |
| Abstract: | Compared with optical image edge detection, there are fewer studies on infrared image edge detection, and most of them are based on traditional methods, such as edge detection operators and mathematical morphology. In essence, they only consider the sharp local changes of infrared images to detect edges, so they are always limited by low-level features. In this paper, an infrared image edge detection algorithm based on deep learning is proposed. Based on the dense extreme inception network for edge detection (DexiNed), the network capacity is reduced by removing the last main block, the image level difference is introduced into the loss function, and the parameters of the loss function are carefully set to optimize the network performance. In addition, by adjusting the natural image edge detection dataset to approximate the infrared image edge detection dataset, the improved model was trained to enhance the edge detection ability. The qualitative evaluation results show that the edge of the infrared image extracted by our method is accurate, precise, rich in detail, and fits human vision. A quantitative evaluation using the structural similarity indexmatrix (SSIM) and feature similarity indexmatrix (FSIM) indexes further shows the advantages of our method compared with other existing methods. |
| |
| Keywords: | |
|
| 点击此处可从《红外技术》浏览原始摘要信息 |
|
点击此处可从《红外技术》下载全文 |
|
