Article ; Online: SAI-YOLO
Computational Intelligence and Neuroscience, Vol
A Lightweight Network for Real-Time Detection of Driver Mask-Wearing Specification on Resource-Constrained Devices
2021 Volume 2021
Abstract: ... correctly on resource-constrained devices, a lightweight target detection network SAI-YOLO is proposed ... function. The experimental results show that SAI-YOLO reduces the number of network parameters and ...
| 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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| Keywords | Computer applications to medicine. Medical informatics ; R858-859.7 ; Neurosciences. Biological psychiatry. Neuropsychiatry ; RC321-571 |
| Subject code | 006 |
| Language | English |
| Publishing date | 2021-01-01T00:00:00Z |
| Publisher | Hindawi Limited |
| Document type | Article ; Online |
| Database | BASE - Bielefeld Academic Search Engine (life sciences selection) |
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