基于改进YOLACT实例分割网络的人耳关键生理曲线提取

Physiological curve extraction of the human ear based on the improved YOLACT

在人耳形状聚类、3D人耳建模、个人定制耳机等相关工作中,获取人耳的一些关键生理曲线和关键点的准确位置非常重要.传统的边缘提取方法对光照和姿势变化非常敏感.本文提出了一种基于ResNeSt和筛选模板策略的改进YOLACT实例分割网络,分别从定位和分割两方面对原始YOLACT算法进行改进,通过标注人耳数据集,训练改进的YOLACT模型,并在预测阶段使用改进的筛选模板策略,可以准确地分割人耳的不同区域并提取关键的生理曲线.相较于其他方法,本文方法在测试图像集上显示出更好的分割精度,且对人耳姿态变化时具有一定的鲁棒性.

In related work, such as human ear shape clustering, three-dimensional human ear modeling, and personal customized headphones, the key physiological curves of the human ear and the accurate positions of key points need to be determined. Moreover, as an important biological feature, the morphological analysis and classification of the human ear are of considerable value for medical work related to the human ear. However, because of the complex morphological structure of the human ear, the generation of a general standard for the morphological structure of the human ear is difficult. This study divided the morphological structure of the human ear into three regions, namely, helix, antihelix, and concha, for instance segmentation and key physiological curve extraction. Traditional edge extraction methods are sensitive to illumination and posture variations. Moreover, the color distribution of one human ear image is relatively consistent. Thus, the transition among the three regions may not be obvious, which will cause poor adaptability for traditional edge extraction methods when extracting the key physiological curves of the human ear. To address this problem, this study proposed an improved YOLACT(You Only Look At CoefficienTs) instance segmentation model based on the ResNeSt backbone and the “screening mask” strategy, which improves the original YOLACT model from two aspects, namely, localization and segmentation. Our ResNeStbased YOLACT model was trained with labeled ear images from the USTB-Helloear image set. In the prediction stage, the original cropping mask strategy was discarded and replaced with our proposed screening mask strategy to ensure the integrity of the edges of the segmentation area. These improvements enhance the accuracy of curve detection and extraction and can accurately segment different regions of the human ear and extract key physiological curves. Compared with other methods, our proposed method shows better segmentation accuracy on the test image set and is more robust to posture variations of the human ear.