Investigation of Edge Computing in Computer Vision-Based Construction Resource Detection
Abstract
:1. Introduction
2. Materials and Methods
2.1. Hardhat Detection
2.2. Edge Computing Device Selection
2.3. Detection Model Embedding
2.4. Evaluation Criteria and Strategies
3. Results
3.1. Training and Validation of the YOLO-v5 Model
3.2. Comparison of Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Method | Applicable Situation | Network Bandwidth Pressure | Real Time | Calculation Mode |
---|---|---|---|---|
Cloud computing | Global | More | High | Large-scale centralization processing |
Edge computing | Local | Less | Low | Small-scale intelligent analysis |
OMEN by HP Laptop 15-dc1xxx | Raspberry Pi 4B | |
---|---|---|
CPU | Intel(R) Core (TM) i5-9300H CPU @ 2.40 GHz [32] | 1.5 GHZ 64-bit quad core ARM Cortex-A72 [29] |
GPU | NVIDIA GeForce GTX 1660Ti [31] | Broadcom VideoCore VI@500 MHz [30] |
Memory | 16 G | 4 G |
Wi-Fi network | 5 GHz double | 5 GHz double |
Size | 36 × 26.3 × 2.5 cm | 85 × 56 mm |
Image Number | Image Size | Local Computer | Raspberry Pi | ||
---|---|---|---|---|---|
Time (s) | Accuracy | Time (s) | Accuracy | ||
1 | 5408 × 3680 | 1.465 | 12/13 | 6.57 | 12/13 |
2 | 4096 × 3072 | 1.271 | 4/4 | 6.47 | 4/4 |
3 | 4096 × 3072 | 1.524 | 5/10 | 5.39 | 5/10 |
4 | 5408 × 3680 | 1.497 | 9/13 | 6.12 | 9/13 |
5 | 5408 × 3680 | 1.619 | 3/3 | 6.40 | 3/3 |
6 | 5408 × 3680 | 1.606 | 4/8 | 6.40 | 4/8 |
7 | 5408 × 3680 | 1.551 | 5/10 | 6.41 | 5/10 |
8 | 4096 × 3072 | 1.304 | 1/2 | 5.13 | 1/2 |
Average | 1.505 | 73.07% | 6.11 | 73.07% |
Video Frames | Video Length | Local Computer | Raspberry Pi | ||
---|---|---|---|---|---|
Time (s) | Accuracy (%) | Time (s) | Accuracy (%) | ||
34,802 | 23 min and 12 s | 33 min and 12 s | 78.06 | 3 h and 14 min | 78.06 |
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Chen, C.; Gu, H.; Lian, S.; Zhao, Y.; Xiao, B. Investigation of Edge Computing in Computer Vision-Based Construction Resource Detection. Buildings 2022, 12, 2167. https://doi.org/10.3390/buildings12122167
Chen C, Gu H, Lian S, Zhao Y, Xiao B. Investigation of Edge Computing in Computer Vision-Based Construction Resource Detection. Buildings. 2022; 12(12):2167. https://doi.org/10.3390/buildings12122167
Chicago/Turabian StyleChen, Chen, Hao Gu, Shenghao Lian, Yiru Zhao, and Bo Xiao. 2022. "Investigation of Edge Computing in Computer Vision-Based Construction Resource Detection" Buildings 12, no. 12: 2167. https://doi.org/10.3390/buildings12122167