Kim Nahyeong, An Jhonghyun
School of Computing, Gachon University, Seongnam-si 1332, Republic of Korea.
Sensors (Basel). 2023 Dec 22;24(1):79. doi: 10.3390/s24010079.
In this study, we propose a knowledge distillation (KD) method for segmenting off-road environment range images. Unlike urban environments, off-road terrains are irregular and pose a higher risk to hardware. Therefore, off-road self-driving systems are required to be computationally efficient. We used LiDAR point cloud range images to address this challenge. The three-dimensional (3D) point cloud data, which are rich in detail, require substantial computational resources. To mitigate this problem, we employ a projection method to convert the image into a two-dimensional (2D) image format using depth information. Our soft label-based knowledge distillation (SLKD) effectively transfers knowledge from a large teacher network to a lightweight student network. We evaluated SLKD using the RELLIS-3D off-road environment dataset, measuring the performance with respect to the mean intersection of union (mIoU) and GPU floating point operations per second (GFLOPS). The experimental results demonstrate that SLKD achieves a favorable trade-off between mIoU and GFLOPS when comparing teacher and student networks. This approach shows promise for enabling efficient off-road autonomous systems with reduced computational costs.
在本研究中，我们提出了一种用于分割越野环境距离图像的知识蒸馏（KD）方法。与城市环境不同，越野地形不规则，对硬件构成更高风险。因此，越野自动驾驶系统需要具备高效的计算能力。我们使用激光雷达点云距离图像来应对这一挑战。三维（3D）点云数据细节丰富，需要大量计算资源。为缓解这一问题，我们采用一种投影方法，利用深度信息将图像转换为二维（2D）图像格式。我们基于软标签的知识蒸馏（SLKD）有效地将知识从大型教师网络转移到轻量级学生网络。我们使用RELLIS-3D越野环境数据集对SLKD进行评估，通过平均交并比（mIoU）和每秒GPU浮点运算次数（GFLOPS）来衡量性能。实验结果表明，在比较教师网络和学生网络时，SLKD在mIoU和GFLOPS之间实现了良好的权衡。这种方法有望实现计算成本降低的高效越野自主系统。
Zotero 插件
