通过不规则的卷积改进了夜间驾驶的语义细分方案
Yang Xuantao1, Han Junying1, Liu Chenzhong1
1College of Information Sciences and Technology, Gansu Agricultural University, Lanzhou, China.
Frontiers in neurorobotics
|June 28, 2023
概括
这项研究通过使用生成模型来修复图像模糊和融合网络层以更好地理解空间来增强夜间驾驶的实时语义细分. 改进后的模型在低光下优异地识别道路状况和运动目标.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 夜间道路状况的实时语义细分因低光和动作模糊而导致性能差.
- 现有的方法难以准确地划分道路场景,并在恶劣的夜间驾驶条件下识别移动物体.
研究的目的:
- 提出一种新的方案,以改善夜间驾驶场景中的实时语义细分性能.
- 为了解决视频图像中光线不足和运动模糊的挑战,以实现自动驾驶的感知.
主要方法:
- 基于生成模型 (DeblurGan) 的模糊信息补充策略被用来恢复图像中丢失的语义信息.
- 开发了一个网络架构,将不同中间层的输出与分配的权重缩放因子结合起来.
- 嵌入了不规则的卷积注意力模块,以精确提取运动目标边界.
主要成果:
- 拟议方案在夜间驾驶数据集上实现了总准确度为89.1%的平均值和94.2%的IOU.
- 性能超过最好的DeepLabv3结果1.3% (平均精度) 和7.2% (IOU).
- 在"可移动" (小体积) 标签上实现了83.0%的准确性,证明了对具有挑战性的目标的有效性.
结论:
- 开发的解决方案有效地应对夜间驾驶固有的挑战,显著提高了模型的感知能力.
- 该方法为夜间自动驾驶环境中的语义细分提供了有价值的技术参考.
- 中间层输出和注意力机制的融合对于强大的夜间场景理解至关重要.
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