论文标题
在密集包装的场景中检测产品的解决方案
A Solution to Product detection in Densely Packed Scenes
论文作者
论文摘要
这项工作是对密集包装的场景数据集Sku-1110k的解决方案。我们的工作是根据Cascade R-CNN修改的。为了解决该问题，我们提出了一种随机的作物策略，以确保采样率和输入量表相对较好，与常规随机作物形成鲜明对比。我们采用了一些技巧，并优化了超参数。为了掌握密集包装的场景的基本特征，我们分析了检测器的阶段，并研究了限制性能的瓶颈。结果，我们的方法在SKU-1110的测试集上获得了58.7 MAP。
This work is a solution to densely packed scenes dataset SKU-110k. Our work is modified from Cascade R-CNN. To solve the problem, we proposed a random crop strategy to ensure both the sampling rate and input scale is relatively sufficient as a contrast to the regular random crop. And we adopted some of trick and optimized the hyper-parameters. To grasp the essential feature of the densely packed scenes, we analysis the stages of a detector and investigate the bottleneck which limits the performance. As a result, our method obtains 58.7 mAP on test set of SKU-110k.
