Rapid Detection of Multi-QR Codes Based on Multistage Stepwise Discrimination and a Compressed MobileNet

Rong-jun, Chen; Huang, Hongxing; Yu, Yongxing; Ren, Jinchang; Wang, Peixian; Zhao, Huimin

doi:10.1109/jiot.2023.3268636

Cited by 21 publications

(10 citation statements)

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“…In the future, to further improve its detection accuracy with the help of sufficiently large data samples, several advanced models such as contrastive learning approach [52], self-attention enhanced deep residual network [53], time-series sequencing method [54], multiscale superpixelwise prophet model [55], and multistage stepwise discrimination with compressed MobileNet [56] will be investigated into the assistance system. Furthermore, additional functions, such as emotion recognition and fatigue detection, will be designed to enhance the overall life quality of visually impaired people.…”

Section: Discussionmentioning

confidence: 99%

An AIoT-Based Assistance System for Visually Impaired People

Li,

Xie,

Chen

et al. 2023

Electronics

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In this work, an assistance system based on the Artificial Intelligence of Things (AIoT) framework was designed and implemented to provide convenience for visually impaired people. This system aims to be low-cost and multi-functional with object detection, obstacle distance measurement, and text recognition achieved by wearable smart glasses, heart rate detection, fall detection, body temperature measurement, and humidity-temperature monitoring offered by an intelligent walking stick. The total hardware cost is approximately $66.8, as diverse low-cost sensors and modules are embedded. Meanwhile, a voice assistant is adopted, which helps to convey detection results to users. As for the performance evaluation, the accuracies of object detection and text recognition in the wearable smart glasses experiments are 92.16% and 99.91%, respectively, and the maximum deviation rate compared to the mobile app on obstacle distance measurement is 6.32%. In addition, the intelligent walking stick experiments indicate that the maximum deviation rates compared to the commercial devices on heart rate detection, body temperature measurement, and humidity-temperature monitoring are 3.52%, 0.19%, and 3.13%, respectively, and the fall detection accuracy is 87.33%. Such results demonstrate that the proposed assistance system yields reliable performances similar to commercial devices and is impressive when considering the total cost as a primary concern. Consequently, it satisfies the fundamental requirements of daily life, benefiting the safety and well-being of visually impaired people.

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Section: Discussionmentioning

confidence: 99%

An AIoT-Based Assistance System for Visually Impaired People

Li,

Xie,

Chen

et al. 2023

Electronics

Self Cite

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“…In future, to apply it in the practical BCI system, the hardware that implements the optimal channel-specific feature and SVM is the main task. In addition, to further enhance the classification accuracy, with the help of sufficiently large feature size, recent advanced approaches, such as self-attention enhanced deep residual network (Xie et al, 2023), multiscale superpixel-wise prophet model (Ma et al, 2023), and multistage stepwise discrimination (Chen et al, 2023), will be developed and investigated.…”

Section: Discussionmentioning

confidence: 99%

An innovative EEG-based emotion recognition using a single channel-specific feature from the brain rhythm code method

Lin

Yan

et al. 2023

Front. Neurosci.

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IntroductionEfficiently recognizing emotions is a critical pursuit in brain–computer interface (BCI), as it has many applications for intelligent healthcare services. In this work, an innovative approach inspired by the genetic code in bioinformatics, which utilizes brain rhythm code features consisting of δ, θ, α, β, or γ, is proposed for electroencephalography (EEG)-based emotion recognition.MethodsThese features are first extracted from the sequencing technique. After evaluating them using four conventional machine learning classifiers, an optimal channel-specific feature that produces the highest accuracy in each emotional case is identified, so emotion recognition through minimal data is realized. By doing so, the complexity of emotion recognition can be significantly reduced, making it more achievable for practical hardware setups.ResultsThe best classification accuracies achieved for the DEAP and MAHNOB datasets range from 83–92%, and for the SEED dataset, it is 78%. The experimental results are impressive, considering the minimal data employed. Further investigation of the optimal features shows that their representative channels are primarily on the frontal region, and associated rhythmic characteristics are typical of multiple kinds. Additionally, individual differences are found, as the optimal feature varies with subjects.DiscussionCompared to previous studies, this work provides insights into designing portable devices, as only one electrode is appropriate to generate satisfactory performances. Consequently, it would advance the understanding of brain rhythms, which offers an innovative solution for classifying EEG signals in diverse BCI applications, including emotion recognition.

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“…Advances in barcode recognition have contributed to the definition of edge detection thresholds, segmentation enhancements for 2D barcodes in text-dense environments, and improvements in rotation handling and positioning accuracy [11][12][13][14][15][16]. In general, traditional image processing techniques fall short in complex environments.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of Two-Dimensional Barcode Restoration Based on Recurrent Feature Reasoning and Structural Fusion Attention Mechanism

Yi,

Chen

2024

Electronics

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In practical scenarios, such as in electronics, where barcodes on electronic component carriers often wear out, and in logistics, where package labels frequently get damaged, this type of damage makes the recognition of two-dimensional (2D) barcodes challenging. In this study, a new repair method was introduced for quick response (QR) and PDF417 codes. In addition, a structural fusion attention (SFA) mechanism with a recurrent feature reasoning network was integrated to enhance structural integrity and recognition rates. The proposed method significantly outperforms existing inpainting models in terms of accuracy and robustness, which is demonstrated by the custom dataset provided by the authors. Notably, the approach ensures near-perfect recognition rates despite extensive structural impairments. It achieves an accuracy of 98% for large-area PDF417 occlusions and maintains a recognition rate of 100% for QR codes with 75–90% structural damage. These findings highlight the exceptional ability of the proposed method to restore 2D barcodes impaired by diverse levels of structural occlusion.

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Rapid Detection of Multi-QR Codes Based on Multistage Stepwise Discrimination and a Compressed MobileNet

Cited by 21 publications

References 50 publications

An AIoT-Based Assistance System for Visually Impaired People

An AIoT-Based Assistance System for Visually Impaired People

An innovative EEG-based emotion recognition using a single channel-specific feature from the brain rhythm code method

Enhancement of Two-Dimensional Barcode Restoration Based on Recurrent Feature Reasoning and Structural Fusion Attention Mechanism

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