2 research outputs found
Smart Inference for Multidigit Convolutional Neural Network based Barcode Decoding
Barcodes are ubiquitous and have been used in most of critical daily
activities for decades. However, most of traditional decoders require
well-founded barcode under a relatively standard condition. While wilder
conditioned barcodes such as underexposed, occluded, blurry, wrinkled and
rotated are commonly captured in reality, those traditional decoders show
weakness of recognizing. Several works attempted to solve those challenging
barcodes, but many limitations still exist. This work aims to solve the
decoding problem using deep convolutional neural network with the possibility
of running on portable devices. Firstly, we proposed a special modification of
inference based on the feature of having checksum and test-time augmentation,
named as Smart Inference (SI) in prediction phase of a trained model. SI
considerably boosts accuracy and reduces the false prediction for trained
models. Secondly, we have created a large practical evaluation dataset of real
captured 1D barcode under various challenging conditions to test our methods
vigorously, which is publicly available for other researchers. The experiments'
results demonstrated the SI effectiveness with the highest accuracy of 95.85%
which outperformed many existing decoders on the evaluation set. Finally, we
successfully minimized the best model by knowledge distillation to a shallow
model which is shown to have high accuracy (90.85%) with good inference speed
of 34.2 ms per image on a real edge device
QuickBrowser: A Unified Model to Detect and Read Simple Object in Real-time
There are many real-life use cases such as barcode scanning or billboard
reading where people need to detect objects and read the object contents.
Commonly existing methods are first trying to localize object regions, then
determine layout and lastly classify content units. However, for simple fixed
structured objects like license plates, this approach becomes overkill and
lengthy to run. This work aims to solve this detect-and-read problem in a
lightweight way by integrating multi-digit recognition into a one-stage object
detection model. Our unified method not only eliminates the duplication in
feature extraction (one for localizing, one again for classifying) but also
provides useful contextual information around object regions for
classification. Additionally, our choice of backbones and modifications in
architecture, loss function, data augmentation and training make the method
robust, efficient and speedy. Secondly, we made a public benchmark dataset of
diverse real-life 1D barcodes for a reliable evaluation, which we collected,
annotated and checked carefully. Eventually, experimental results prove the
method's efficiency on the barcode problem by outperforming industrial tools in
both detecting and decoding rates with a real-time fps at a VGA-similar
resolution. It also did a great job expectedly on the license-plate recognition
task (on the AOLP dataset) by outperforming the current state-of-the-art method
significantly in terms of recognition rate and inference time.Comment: Accepted at 2021 International Joint Conference on Neural Networks
(IJCNN