实时实例分割的深度轮廓段落匹配算法

Deep contour fragment matching algorithm for real-time instance segmentation

针对实例分割算法在进行轮廓收敛时,普遍存在目标遮挡增加轮廓处理的时间以及降低检测框的准确性的问题。本文提出一种实时实例分割的算法,在处理轮廓中增加段落匹配、目标聚合损失函数和边界系数模块。首先对初始轮廓进行分段处理,在每一个段落内进行分配局部地面真值点,实现更自然、快捷和平滑的变形路径。其次利用目标聚合损失函数和边界系数模块对存在目标遮挡的物体进行预测,给出准确的检测框。最后利用循环卷积与Snake模型对匹配过的轮廓进行收敛,对顶点进行迭代计算得到分割结果。本文算法在COCO、Cityscapes、Kins等多个数据集上进行评估,其中COCO数据集上取得32.6%mAP和36.3 f/s的结果,在精度与速度上取得最佳平衡。

During the instance segmentation for contour convergence,it is a general problem that target occlusion increases the time for contour processing and reduces the accuracy of the detection box.This paper proposes an algorithm for real-time instance segmentation,adding fragment matching,target aggregation loss function and boundary coefficient modules to the processing contour.Firstly,fragment matching is performed on the initial contour formed by evenly spaced points,and local ground truth points are allocated in each fragment to achieve a more natural,faster,and smoother deformation path.Secondly,the target aggregation loss function and the boundary coefficient modules are used to predict the objects in the presence of object occlusion and give an accurate detection box.Finally,circular convolution and Snake model are used to converge the matched contours,and then the vertices are iteratively calculated to obtain segmentation results.The proposed method is evaluated on multiple data sets such as Cityscapes,Kins,COCO,et al,among which 30.7 mAP and 33.1 f/s results are obtained on the COCO dataset,achieving a compromise between accuracy and speed.