Day #282 - Retail Product Detection / Retail Object Detection

Paper #1 - Automatic Detection of Out-Of-Shelf Products in the Retail Sector Supply Chain

Rule-based information system

• OOS Contribution factors - Measurement of product availability, Measurement of shelf availability
• Approach - Radio-Frequency Identification based
• Rule - “IF (a product is fast-moving) AND (has low sales volatility) AND (POS sales = 0 for today) THEN the product is OOS
• IF Fastmoving product sales count is zero then there is a problem
• Detection approach = Historical data -> Patterns -> Rules = Apply on Current Data

Paper #2 - Retail Shelf Analytics Through Image Processing and Deep Learning
Analysis

• Tasks - Automatic product checkout using segmentation, Object detection of products on store shelves
• Approach - Shelf Image -> Detector for regions (Class, BBox, Mask) -> Crop Each Region -> Object -> Feature Extractor -> KNN Classifier

Paper #3 - A deep learning pipeline for product recognition on store shelves
Analysis

• Shelf Image -> Region proposals -> Crop -> Reference Images -> Refinement -> Detection

Paper #4 - Planogram Compliance Checking Based on Detection of Recurring Patterns
Analysis

• Shelf Image -> Region Partition -> Recurring Pattern Detection -> Compliance Checking

High Level Recommendations (Apply Combination of techniques)

• Shelf Image -> Region Partition -> Region proposals -> Detect Recurring Pattern, Reference Images for refinement -> Prediction

Project Analysis 
The Shelf Detector System For Retail Stores Using Object Detection

pip install -r requirements.txt
python train_obj_detector.py testNutella1

Code Details - https://github.com/bobquest33/dlib_obj_count/blob/master/nutella.pdf
Tool Used - https://imglab.in/

Interesting Read
Retail Product Recognition on Supermarket Shelves

Paper #5 - Rethinking Object Detection in Retail Stores
Key Notes

• Simultaneously object localization and counting, abbreviated as Locount
• Algorithms to localize groups of objects of interest with the number of instances
• Most of the state-of-the-art object detectors use non-maximal suppression (NMS) to post-process object proposals to produce final detections

New Approach

• Cascaded localization and counting network (CLCNet)
• Localize groups of objects of interest with the numbers of instances

Dataset

• Grozi-120 dataset
• Freiburg Groceries dataset
• GameStop dataset
• Retail-121 dataset
• Sku110k dataset
• TGFS dataset

Cascade R-CNN [1] proposes a multi-stage object detection architecture, which is formed by a sequence of detectors trained with increasing IoU thresholds

Locount Dataset

• 140 common commodities, including 9 big subclasses
• Cascaded localization and counting network (CLCNet
• count-regression strategy for counting
• count-classification strategy for counting
• Locount to localize groups of objects with the instance numbers, which is more practical in retail scenarios

Happy Learning!!!