IYOLO-NL: An improved you only look once and none left object detector for real-time face mask detection

Zhou, Yan

doi:10.1016/j.heliyon.2023.e19064

Cited by 7 publications

(1 citation statement)

References 31 publications

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“…It makes use of the truncated gradient flow technique and the crossstage feature fusion strategy to increase the network's learning capacity, lessen the impact of redundant information, and improve the variability of learned features across various network levels. The introduction of the C2fGhost module greatly reduces the number of model parameters needed as well as the computational effort by greatly reducing the number of common 3 × 3 convolutions [24,25]. kernels are employed to take feature data out of the input feature map.…”

Section: The C2fghost Lightweight Modulementioning

confidence: 99%

Quality Detection and Grading of Rose Tea Based on a Lightweight Model

Ding,

Chen,

Gui

et al. 2024

Foods

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Rose tea is a type of flower tea in China’s reprocessed tea category, which is divided into seven grades, including super flower, primary flower, flower bud, flower heart, yellow flower, scattered flower, and waste flower. Grading rose tea into distinct quality levels is a practice that is essential to boosting their competitive advantage. Manual grading is inefficient. We provide a lightweight model to advance rose tea grading automation. Firstly, four kinds of attention mechanisms were introduced into the backbone and compared. According to the experimental results, the Convolutional Block Attention Module (CBAM) was chosen in the end due to its ultimate capacity to enhance the overall detection performance of the model. Second, the lightweight module C2fGhost was utilized to change the original C2f module in the neck to lighten the network while maintaining detection performance. Finally, we used the SIoU loss in place of the CIoU loss to improve the boundary regression performance of the model. The results showed that the mAP, precision (P), recall (R), FPS, GFLOPs, and Params values of the proposed model were 86.16%, 89.77%, 83.01%, 166.58, 7.978, and 2.746 M, respectively. Compared with the original model, the mAP, P, and R values increased by 0.67%, 0.73%, and 0.64%, the GFLOPs and Params decreased by 0.88 and 0.411 M, respectively, and the speed was comparable. The model proposed in this study also performed better than other advanced detection models. It provides theoretical research and technical support for the intelligent grading of roses.

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Section: The C2fghost Lightweight Modulementioning

confidence: 99%

Quality Detection and Grading of Rose Tea Based on a Lightweight Model

Ding,

Chen,

Gui

et al. 2024

Foods

View full text Add to dashboard Cite

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Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection

Sayem,

Islam,

Naznine

et al. 2024

Neural Comput & Applic

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Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key considerations in processing, managing, and utilizing city waste. Deep learning technologies have emerged as viable solutions to address waste management issues by reducing labor costs and automating complex tasks. However, the limited number of trash image categories and the inadequacy of existing datasets have constrained the proper evaluation of machine learning model performance across a large number of waste classes. In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. For the waste classification task, we proposed a novel dual-stream network that outperformed several state-of-the-art models, achieving an overall classification accuracy of 83.11%. Additionally, we introduced the GELAN-E (generalized efficient layer aggregation network) model for waste object detection tasks, obtaining a mean average precision (mAP50) of 63%, surpassing other state-of-the-art detection models. These advancements demonstrate significant progress in the field of intelligent waste management, paving the way for more efficient and effective solutions.

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A YOLO-NL object detector for real-time detection

Zhou

2024

Expert Systems with Applications

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IYOLO-NL: An improved you only look once and none left object detector for real-time face mask detection

Cited by 7 publications

References 31 publications

Quality Detection and Grading of Rose Tea Based on a Lightweight Model

Quality Detection and Grading of Rose Tea Based on a Lightweight Model

Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection

A YOLO-NL object detector for real-time detection

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