论文标题
将面膜R-CNN的性能提高长而薄的法医痕迹，并进行预分段和IOU区域合并
Boosting Mask R-CNN Performance for Long, Thin Forensic Traces with Pre-Segmentation and IoU Region Merging
论文作者
论文摘要
Mask R-CNN最近在实例细分领域取得了巨大成功。然而，算法的弱点也反复指出，尤其是在分割长而稀疏的物体的分割中，其方向不仅是水平或垂直的。我们在这里提出了一种方法，该方法通过首先使用PSPNET算法对图像进行预分段，从而显着提高了算法的性能。为了进一步改善其预测，我们以培训策略的形式开发了自己的成本功能和启发式方法，这可以防止所谓的（早期）过度拟合并实现更有针对性的融合。此外，由于图像的较高差异，尤其是对于PSPNET，我们旨在制定高鲁棒性和概括的策略，此处也介绍了这些策略。
Mask R-CNN has recently achieved great success in the field of instance segmentation. However, weaknesses of the algorithm have been repeatedly pointed out as well, especially in the segmentation of long, sparse objects whose orientation is not exclusively horizontal or vertical. We present here an approach that significantly improves the performance of the algorithm by first pre-segmenting the images with a PSPNet algorithm. To further improve its prediction, we have developed our own cost functions and heuristics in the form of training strategies, which can prevent so-called (early) overfitting and achieve a more targeted convergence. Furthermore, due to the high variance of the images, especially for PSPNet, we aimed to develop strategies for a high robustness and generalization, which are also presented here.
