Road Obstacle Object Detection Based on Improved YOLO V4
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22. Feb. 2021
Über diesen Artikel
Online veröffentlicht: 22. Feb. 2021
Seitenbereich: 18 - 25
DOI: https://doi.org/10.21307/ijanmc-2021-023
Schlüsselwörter
© 2021 Xiao Zuo et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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Comparison of test results
| AP(%) | Car | Bus | Person | Motorbike | Bicycle | mAP(%) |
|---|---|---|---|---|---|---|
| yolo v4 | 0.98 | 0.93 | 0.92 | 0.81 | 0.51 | 82.95 |
| Improved yolo v4 | 0.99 | 0.93 | 0.92 | 0.81 | 0.58 | 84.98 |
Environment configuration
| Hardware environment | processor |
Intel(R) XEON W-2133 |
|---|---|---|
| Software Environment | operating system | Ubuntu 16.04 |
| Deep learning framework | Tensorflow-gpu | |
| Programming language | Python | |
| translater | Pycharm2019.1 |
Main network parameter values
| Parameter | Value | Parameter | Value |
|---|---|---|---|
| LEARN_RATE_INIT | 1e-4 | MOVING_AVE_DECAY | 0.9999 |
| LEARN_RATE_END | 1e-6 | STAGE_EPOCHS | 100 |