A Fully Convolutional Network-Based Tube Contour Detection Method Using Multi-Exposure Images

• Main Authors: Xiaoqi Cheng, Junhua Sun, Fuqiang Zhou
• Format: Article
• Language: English
• Published: MDPI AG 2021-06-01
• Series: Sensors
• Subjects: fully convolutional network; tube contour detection; multi-exposure images; U-Net; dilation operation
• Online Access: https://www.mdpi.com/1424-8220/21/12/4095

The tube contours in two-dimensional images are important cues for optical three-dimensional reconstruction. Aiming at the practical problems encountered in the application of tube contour detection under complex background, a fully convolutional network (FCN)-based tube contour detection method is proposed. Multi-exposure (ME) images are captured as the input of FCN in order to get information of tube contours in different dynamic ranges, and the U-Net type architecture is adopted by the FCN to achieve pixel-level dense classification. In addition, we propose a new loss function that can help eliminate the adverse effects caused by the positional deviation and jagged morphology of tube contour labels. Finally, we introduce a new dataset called multi-exposure tube contour dataset (METCD) and a new evaluation metric called dilate inaccuracy at optimal dataset scale (DIA-ODS) to reach an overall evaluation of our proposed method. The experimental results show that the proposed method can effectively improve the integrity and accuracy of tube contour detection in complex scenes.