Detecting retail products in situ using CNN without human effort labeling

TL;DR

This paper presents a CNN-based method for detecting retail products in situ across 324 categories without requiring manual bounding box annotations, utilizing algorithms for bounding box extraction and occlusion simulation.

Contribution

It introduces a novel approach that eliminates the need for human effort in labeling bounding boxes for training CNNs in retail product detection.

Findings

Effective detection of 324 retail product categories in situ

No manual bounding box labeling required for training

Applicable to other scenarios beyond retail detection

Abstract

CNN is a powerful tool for many computer vision tasks, achieving much better result than traditional methods. Since CNN has a very large capacity, training such a neural network often requires many data, but it is often expensive to obtain labeled images in real practice, especially for object detection, where collecting bounding box of every object in training set requires many human efforts. This is the case in detection of retail products where there can be many different categories. In this paper, we focus on applying CNN to detect 324-categories products in situ, while requiring no extra effort of labeling bounding box for any image. Our approach is based on an algorithm that extracts bounding box from in-vitro dataset and an algorithm to simulate occlusion. We have successfully shown the effectiveness and usefulness of our methods to build up a Faster RCNN detection model. Similar idea is also applicable in other scenarios.