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Face mask detection model using deep learning on edge computing
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AI
DOI：10.1007/s11227-026-08594-3.png)
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Abstract
En 中文
Deploying deep learning models for real-time face mask detection on edge devices requires balancing high accuracy with limited computational resources. While existing studies evaluate model-hardware compatibility, a significant research gap exists in understanding the systemic interplay between peripheral I/O and hardware-accelerated kernels, which often serve as hidden performance throttles. This study completes the system orchestration of a High-Performance Embedded Computing (HPEC) framework to provide a systematic reference for hardware/software selection. We evaluate the performance trade-offs of object detection (YOLOv4) and image classification (Inception V3, MobileNet, VGG16) across standard CPU-based systems and GPU-accelerated edge environments, specifically the NVIDIA Jetson Xavier NX and Raspberry Pi 4 with Intel NCS 2. Experimental results demonstrate that while increasing YOLOv4 resolution to 608 pixels maximizes mAP to 90.01%, it imposes a 14.5 BFLOPS computational penalty. A critical contribution of this work is the quantification of CPU-bound I/O bottlenecks on ARM-based architectures, where inefficient video decoding was found to halve GPU throughput regardless of model efficiency. Furthermore, we establish a 3.8 $$\times $$ energy efficiency improvement (2.72 J/frame) on the Jetson Xavier NX compared to workstation baselines. These evaluations offer optimized HPEC (High-Performance Embedded Computing) deployment on the Jetson, which is 3.8 $$\times $$ more efficient than standard workstation-based inference.
Keywords:
Transfer learning
Object detection
Face mask detection
CNN
YOLO
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