Grocery Product Recognition to Aid Visually Impaired People

Machado, André de Lima; Aires, Kelson R. T.; Veras, Rodrigo; Neto, Laurindo Britto

doi:10.5753/wvc.2021.18896

Cited by 2 publications

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Machado et al [41] also devised a system for recognizing products intended for visually impaired individuals. Their findings indicated that the ResNet-50-based approach outperformed other deep learning models.…”

Section: Related Workmentioning

confidence: 99%

Batch Normalization Free Rigorous Feature Flow Neural Network for Grocery Product Recognition

Selvam,

Faheem,

Dakshinamurthi

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Automatic product recognition is crucial in advancing economic and social fronts due to its superior reliability and time-saving nature compared to manual operations. The precise organization of products on store shelves is essential for boosting sales and ensuring customer satisfaction. However, verifying that the physical arrangement aligns with the ideal plan is a costly and time-consuming task for store personnel. In the computer vision domain, detecting products in scene images poses a considerable challenge, particularly when dealing with grocery items displayed on store shelves. The arrangement of products often presents crowded environments with numerous identical objects placed closely together. This study illustrates the ongoing challenge of identifying specific objects in complex situations despite using advanced object detection systems. The proposed framework consists of a three-stage pipeline. The initial stage incorporates a cutting-edge product detection algorithm, YOLOv5, to locate multiple grocery objects. The proposed OD-Refiner layer in the second stage identifies the missed retail object and rectifies overlapping bounding boxes of YOLOv5. The OCR-based object recognizer called Batch Normalization Free Rigorous Feature Flow Neural Network (BNFRNN) is proposed in the final stage of the pipeline. The performance of the proposed framework was evaluated using a benchmark dataset, WebMarket. The proposed framework outperforms current state-of-the-art approaches by achieving a precision score of 92.56%, recall of 85.64%, and F-score of 88.97%.

show abstract

Section: Related Workmentioning

confidence: 99%

Batch Normalization Free Rigorous Feature Flow Neural Network for Grocery Product Recognition

Selvam,

Faheem,

Dakshinamurthi

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

A Deep Learning Framework for Grocery Product Detection and Recognition

Selvam

Koilraj

2022

Food Anal. Methods

View full text Add to dashboard Cite

Object detection and recognition are the most important and challenging problems in computer vision.The remarkable advancements in deep learning techniques have signi cantly accelerated the momentum of object detection/recognition in recent years. Meanwhile, scene text detection/recognition is also a critical task in computer vision and has gotten more attention from many researchers due to its wide range of applications. This work focuses on detecting and recognizing multiple retail products stacked on the shelves and off the shelves in the grocery stores by identifying the label texts. In this paper, we proposed a new framework is composed of three modules: (a) Retail product detection, (b) Product-text detection (c) Product-text recognition. In the rst module, on-the-shelf and off-shelf retail products are detected using the YOLOv5 object detection algorithm. In the second module, we improve the performance of the state-of-the-art text detection algorithm named, "TextSnake", by replacing the backbone network (ResNet50 + FPN) and a post-processing technique, WHBBR (Width Height based Bounding Box Reconstruction), is proposed to detect regular and irregular text. In the nal module, we used a text recognition network named "SCATTER" to recognize the retail product's text information. The YOLOv5 algorithm accurately detects both on-the-shelf and off-the-shelf grocery products from the video frames and the static images. The experimental results show that the proposed text reconstruction approach WHBBR improves the performance of the state-of-the-art techniques on both regular and irregular text. The enhanced text detection and incorporated text recognition methods greatly support our proposed framework to recognize the on-the-shelf retail products by extracting product information such as product name, brand name, price, expiring date, etc. The recognized text contexts around the retail products can be used as the identi er to distinguish the product.

show abstract

Grocery Product Recognition to Aid Visually Impaired People

Cited by 2 publications

References 9 publications

Batch Normalization Free Rigorous Feature Flow Neural Network for Grocery Product Recognition

Batch Normalization Free Rigorous Feature Flow Neural Network for Grocery Product Recognition

A Deep Learning Framework for Grocery Product Detection and Recognition

Contact Info

Product

Resources

About