Precise Detection in Densely Packed Scenes

Man-made scenes can be densely packed, containing numerous objects, often identical, positioned in close proximity. We show that precise object detection in such scenes remains a challenging frontier even for state-of-the-art object detectors. We propose a novel, deep-learning based method for precise object detection, designed for such challenging settings. Our contributions include: (1) A layer for estimating the Jaccard index as a detection quality score; (2) a novel EM merging unit, which uses our quality scores to resolve detection overlap ambiguities; finally, (3) an extensive, annotated data set, SKU-110K, representing packed retail environments, released for training and testing under such extreme settings. Detection tests on SKU-110K and counting tests on the CARPK and PUCPR+ show our method to outperform existing state-of-the-art with substantial margins. The code and data will be made available on \url{}.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Object Counting CARPK Soft-IoU + EM-Merger unit MAE 6.77 # 2
RMSE 8.52 # 2
Dense Object Detection SKU-110K Soft-IoU + EM-Merger unit AP 0.492 # 2
AP75 0.556 # 2


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