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Graph-based non-maximal suppression for detecting products on the rack
Pattern Recognition Letters ( IF 5.1 ) Pub Date : 2020-10-02 , DOI: 10.1016/j.patrec.2020.09.023
Bikash Santra , Avishek Kumar Shaw , Dipti Prasad Mukherjee

Identification of stacked retail products from the images of racks of supermarket is a challenging computer vision problem. Regions with convolutional neural network features (R-CNN) generate (mostly overlapped) region proposals around the products on the rack. Subsequently, the region proposals are classified using a convolutional neural network. In the end, R-CNN implements a greedy non-maximal suppression (greedy-NMS) for disambiguating the overlapping proposals. Greedy-NMS discards the proposals (with lower classification scores) that are overlapped with the proposal with higher classification score. This greedy approach often eliminates the (geometrically) better fitted region proposals with (marginally) lower classification scores. This paper introduces a novel graph-based non-maximal suppression (G-NMS) that removes this critical bottleneck of greedy-NMS by looking not only at the classification scores but also at the product classes of the overlapping region proposals. G-NMS first determines the potential confidence scores (pc-scores) of the region proposals by defining the groups of overlapping regions. Subsequently, a directed acyclic graph (DAG) is strategically constructed with the proposals utilizing their pc-scores and overlapping groups. Eventually the maximum weighted path of the DAG provides the products that are present in the rack. The results of our extensive experiments confirm that the proposed scheme is better up to around 7% on one large In-house and three benchmark datasets of retail products. Additionally, the efficacy of our proposed GNMS is also analyzed on four benchmark datasets for detecting generic objects.



中文翻译:

基于图的非最大抑制来检测货架上的产品

从超市的货架图像识别堆叠的零售产品是一个具有挑战性的计算机视觉问题。具有卷积神经网络特征(R-CNN)的区域会在货架上的产品周围生成(大部分重叠)区域建议。随后,使用卷积神经网络对区域建议进行分类。最后,R-CNN实施了贪婪的非最大抑制(greedy-NMS),以消除重叠提案的歧义。Greedy-NMS丢弃与具有较高分类分数的投标重叠的投标(具有较低分类分数)。这种贪婪的方法通常会消除(几何上)拟合度更高的区域建议,而分类分数(略低)。本文介绍了一种新颖的基于图的非最大抑制(G-NMS),它不仅通过查看分类得分,而且还通过查看重叠区域提案的产品类别,消除了贪婪NMS的关键瓶颈。G-NMS首先确定通过定义重叠区域的组,区域提案的潜在置信度得分(pc分数)。随后,利用提案的pc得分和重叠组从战略上构造了有向无环图(DAG)。最终,DAG的最大加权路径提供了机架中存在的产品。我们大量实验的结果证实,在一个大型内部零售产品和三个基准零售数据集上,所提出的方案更好地达到了7%左右。此外,我们还在四个基准数据集上分析了我们提出的GNMS的功效,以检测通用对象。

更新日期:2020-10-02
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