A Solution to Product detection in Densely Packed Scenes
This work addresses product detection for retail or inventory management, but it is incremental as it builds on Cascade R-CNN with minor modifications.
The paper tackles product detection in densely packed scenes using the SKU-110k dataset, achieving a result of 58.7 mAP on the test set.
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.