量子LaneNet:一个基于FPGA的640FPS和34GOPS/W的CNN加速器用于车道检测
Duc Khai Lam1,2, Cam Vinh Du1,2, Hoai Luan Pham3
1Computer Engineering Department, University of Information Technology, Ho Chi Minh City 700000, Vietnam.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
本研究介绍了一种轻量级的深度学习模型,用于高效的自动驾驶车道检测. 这种新的方法在FPGA硬件上实现了高精度,最小的后处理和实时性能.
科学领域:
- 计算机视觉 计算机视觉
- 自主系统 自主系统
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 车道检测对于自动驾驶汽车至关重要.
- 现有的深度学习方法通常需要大量的后处理,或者对实时应用来说过于复杂.
研究的目的:
- 开发一个轻量级的卷积神经网络,以实现高效的车道检测.
- 解决基于细分的方法和复杂的模型架构的局限性.
主要方法:
- 提出了一个轻量级的卷积神经网络,具有简单的车道表示输出.
- 在FPGA (Virtex-7 VC707) 上实现了一个硬件加速器,使用8位数据量化,循环解滚和管道计算.
- 在TuSimple数据集上实现了93.53%的准确性.
主要成果:
- 拟议的模型在TuSimple数据集上实现了93.53%的准确性.
- 在FPGA实施过程中,在640FPS的低功耗 (10.309W).
- 证明了高的计算吞吐量 (345.6 GOPS) 和能源效率 (33.52 GOPS/W).
结论:
- 轻量级网络为自动驾驶汽车的实时车道检测提供了有效的解决方案.
- 硬件加速显著优化了处理速度和功率效率.
- 该方法克服了现有的深度学习方法用于车道检测的局限性.
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