A Solution to Product detection in Densely Packed Scenes

2020/07/23 by Tianze Rong, Rong, Tianze, Yanjia Zhu +5
Computer Science · #Advanced Image and Video Retrieval Techniques #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Video Surveillance and Tracking Methods

paper · pdf · doi:10.48550/arxiv.2007.11946

openalex publication_date 2020/07/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal\n Networks
• MMDetection: Open MMLab Detection Toolbox and Benchmark
• EfficientDet: Scalable and Efficient Object Detection

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