基于无锚框分割网络改进的实例分割方法

在无人驾驶应用场景中，现有无锚框实例分割方法存在大目标特征覆盖小目标特征、缺少两阶段检测器中的感兴趣区域对齐操作、忽略类别分支对掩膜分支提供的位置和空间信息等问题，导致特征提取不充分且无法准确获取目标区域。提出一种改进的无锚框实例分割方法。结合可变形卷积，设计编码-解码特征提取网络提取高分辨率特征，以增强对小目标特征的提取能力，并采用空洞卷积和合并连接的方式，在不增加计算量的前提下有效融合多种分辨率的特征。在此基础上，将注意力机制引入到类别分支中，同时设计结合空间信息和通道信息的信息增强模块，以提高目标检测能力。实验结果表明，该方法在COCO 2017和Cityscapes数据集上平均精度和平均交并比分别为41.1%和83.3%，相比Mask R-CNN、SOLO、Yolact等方法，能够有效改进实例分割效果并具有较优的鲁棒性。

Improved Instance Segmentation Method Based on Anchor-Free Segmentation Network

In autonomous driving application scenarios, the existing anchor-free instance segmentation methods have problems such as large target features covering small target features, lack of a Region Of Interest (ROI)-Align operation in the two-stage detector, ignoring the position and spatial information provided by the regression branch to the mask branch, resulting in insufficient feature extraction and unable to accurately obtain the target region.An improved method for instance, anchor-free segmentation is proposed.Combined with deformable convolution, a encoder-decoder feature extraction network is designed to extract high-resolution features and enhance the extraction ability of small target features.The dilated convolution and merging connection method is adopted to effectively fuse the features of multiple resolutions without increasing the computation amount.On this basis, the attention mechanism is introduced into the regression branch, and an information enhancement module combining spatial and channel information is designed to improve the ability of target detection.The experimental results show that the Average Precision(AP) and mean Intersection over Union(mIoU) of the proposed method on the COCO 2017 and Cityscapes datasets are 41.1% and 83.3%, respectively.Compared with Mask R-CNN, SOLO, Yolact, and other methods, the proposed method can effectively improve the effect of instance segmentation and has better robustness.