School of Mechanical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China.
Sensors (Basel). 2020 May 4;20(9):2618. doi: 10.3390/s20092618.
A line structured light sensor (LSLS) is generally constituted of a laser line projector and a camera. With the advantages of simple construction, non-contact, and high measuring speed, it is of great perspective in 3D measurement. For traditional LSLSs, the camera exposure time is usually fixed while the surface properties can be varied for different measurement tasks. This would lead to under/over exposure of the stripe images or even failure of the measurement. To avoid these undesired situations, an adaptive control method was proposed to modulate the average stripe width (ASW) within a favorite range. The ASW is first computed based on the back propagation neural network (BPNN), which can reach a high accuracy result and reduce the runtime dramatically. Then, the approximate linear relationship between the ASW and the exposure time was demonstrated via a series of experiments. Thus, a linear iteration procedure was proposed to compute the optimal camera exposure time. When the optimized exposure time is real-time adjusted, stripe images with the favorite ASW can be obtained during the whole scanning process. The smoothness of the stripe center lines and the surface integrity can be improved. A small proportion of the invalid stripe images further proves the effectiveness of the control method.
线结构光传感器（LSLS）通常由激光线投影仪和相机组成。它具有结构简单、非接触和测量速度快等优点，在三维测量中具有广阔的应用前景。对于传统的 LSLS，相机的曝光时间通常是固定的，而表面特性可以根据不同的测量任务而变化。这可能会导致条纹图像曝光不足/过度，甚至测量失败。为了避免这些不理想的情况，提出了一种自适应控制方法来调节平均条纹宽度（ASW）在一个理想的范围内。首先基于反向传播神经网络（BPNN）计算 ASW，该方法可以达到高精度的结果，并大大减少运行时间。然后，通过一系列实验证明了 ASW 与曝光时间之间的近似线性关系。因此，提出了一种线性迭代过程来计算最佳相机曝光时间。实时调整优化的曝光时间后，可以在整个扫描过程中获得具有理想 ASW 的条纹图像。条纹中心线的平滑度和表面完整性可以得到改善。一小部分无效的条纹图像进一步证明了控制方法的有效性。
