ArticleComputational intelligence and neuroscience2021
SAI-YOLO: A Lightweight Network for Real-Time Detection of Driver Mask-Wearing Specification on Resource-Constrained Devices.
Article in Computational intelligence and neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Robust face mask detection in complex scenarios using YOLOv8 and context-aware convolutions.Scientific reports · 2025Article
- GEB-YOLO: a novel algorithm for enhanced and efficient detection of foreign objects in power transmission lines.Scientific reports · 2024Article
- Optimizing Road Safety: Advancements in Lightweight YOLOv8 Models and GhostC2f Design for Real-Time Distracted Driving Detection.Sensors (Basel, Switzerland) · 2023Article
- Article
- PIS-YOLO: Real-Time Detection for Medical Mask Specification in an Edge Device.Computational intelligence and neuroscience · 2022Article
- Face Mask-Wearing Detection Model Based on Loss Function and Attention Mechanism.Computational intelligence and neuroscience · 2022Review
- A Data Augmentation Method for Prohibited Item X-Ray Pseudocolor Images in X-Ray Security Inspection Based on Wasserstein Generative Adversarial Network and Spatial-and-Channel Attention Block.Computational intelligence and neuroscience · 2022Article
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Authors and funding
7 authors.
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Abstract
Frequent occurrence and long-term existence of respiratory diseases such as COVID-19 and influenza require bus drivers to wear masks correctly during driving. To quickly detect whether the mask is worn correctly on resource-constrained devices, a lightweight target detection network SAI-YOLO is proposed. Based on YOLOv4-Tiny, the network incorporates the Inception V3 structure, replaces two CSPBlock modules with the RES-SEBlock modules to reduce the number of parameters and computational difficulty, and adds a convolutional block attention module and a squeeze-and-excitation module to extract key feature information. Moreover, a modified ReLU (M-ReLU) activation function is introduced to replace the original Leaky_ReLU function. The experimental results show that SAI-YOLO reduces the number of network parameters and calculation difficulty and improves the detection speed of the network while maintaining certain recognition accuracy. The mean average precision (mAP) for face-mask-wearing detection reaches 86% and the average precision (AP) for mask-wearing normative detection reaches 88%. In the resource-constrained device Raspberry Pi 4B, the average detection time after acceleration is 197 ms, which meets the actual application requirements.
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Registered trials
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