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.

翻译：这项工作是密布场景数据集 SKU- 110k 的解决方案。 我们的工作由Cascade R- CNN 修改。 为了解决问题,我们提出了一个随机作物战略,以确保抽样率和输入比例相对而言都足以与常规随机作物形成对比。 我们采用了一些伎俩,优化了超参数。 为了了解密布场景的基本特征, 我们分析探测器的阶段, 并调查限制性能的瓶颈。 因此, 我们的方法在SKU-110k测试集上获得了58.7 mAP。