Xicai Li, Qinqin Wu, Yuanqing Wang
Opt Express. 2021 Feb 1;29(3):3855-3872. doi: 10.1364/OE.405600.
We present a calibration plate for the binocular vision system, which is composed of a long-wavelength infrared camera and a visible spectrum camera with different resolutions. The calibration plate mainly consists of a white low-temperature aluminum plate with 7×7 round through-holes, a black high-temperature stainless steel plate, and a heating plate. It can be captured by the long-wavelength infrared camera and visible spectrum camera simultaneously. In order to reduce the influence of thermal crosstalk on the edge and angle sharpness of the thermal image of the chessboard calibration plate, we use the round through-holes to replace the black-white squares in the chessboard calibration plate. Based on the fabricated calibration plate, we also propose a related calibration method. The proposed method can quickly detect the calibration plate by using the YOLO-V4 neural network. The affine transformation is performed to get the front view of the calibration plate, and a novel circular detection strategy based on arc level instead of pixel-level is adopted to detect the edges of the round through-holes in the calibration plate. The centers of round through-holes are detected, and the parameters of the cameras are calculated according to the coordinates of the centers in the image coordinate system. The simulation experiments and error analysis have been done to verify the centers detection method. The simulation results show that the error of center detection is always less than 1.4 (pixel). In order to further verify the performance of the calibration plate and the proposed calibration method, a binocular vision system based on long-wavelength infrared camera and visible spectrum camera is fabricated, and the verification experiments have been done. In experiments, our calibration plate and the proposed method are compared with the famous Zhang's method. The calibration's average overall mean errors of the visible spectrum camera and long-wavelength infrared camera are about 0.0126 (pixel) and 0.0238 (pixel), and they are respectively decreased by 78.13% and 81.93% compared with Zhang's method. The re-projection error of the binocular vision system is about 0.548 (pixel), which is decreased by 24.52% compared with Zhang's method. The average calibration time of the proposed method is about 0.26s.
我们展示了一种用于双目视觉系统的校准板，它由具有不同分辨率的长波长红外相机和可见光谱相机组成。该校准板主要由一块带有7×7圆形通孔的白色低温铝板、一块黑色高温不锈钢板和一块加热板组成。它可以同时被长波长红外相机和可见光谱相机捕获。为了减少热串扰对棋盘校准板热图像边缘和角度清晰度的影响，我们使用圆形通孔代替棋盘校准板中的黑白方块。基于制作的校准板，我们还提出了一种相关的校准方法。所提出的方法可以使用YOLO-V4神经网络快速检测校准板。进行仿射变换以获得校准板的正视图，并采用一种基于弧级而非像素级的新颖圆形检测策略来检测校准板中圆形通孔的边缘。检测圆形通孔的中心，并根据图像坐标系中中心的坐标计算相机参数。进行了仿真实验和误差分析以验证中心检测方法。仿真结果表明，中心检测误差始终小于1.4（像素）。为了进一步验证校准板和所提出的校准方法的性能，制作了一个基于长波长红外相机和可见光谱相机的双目视觉系统，并进行了验证实验。在实验中，将我们的校准板和所提出的方法与著名的张氏方法进行了比较。可见光谱相机和长波长红外相机校准的平均总体平均误差分别约为0.0126（像素）和0.0238（像素），与张氏方法相比分别降低了78.13%和81.93%。双目视觉系统的重投影误差约为0.548（像素），与张氏方法相比降低了24.52%。所提出方法的平均校准时间约为0.26秒。
