Some non-digitized measuring instruments such as pointer meters are utilized by some farmlands by virtue of their high stability and accuracy, and the data collection depending on the human movements requires considerable time and effort and has a low technology level. Therefore, an intelligent pointer meter indication reading and automatic collection method is proposed using machine vision and wireless sensor network (WSN) technology. An intelligent pointer meter indication reading device consisting of an image acquisition module, an intelligent control center, a wireless transmission module, and a power module was developed, which could accomplish intelligent meter indication reading and automatic data uploading. Furthermore, a pointer meter indication reading algorithm for a resource-limited environment is proposed. Firstly, the pointer and the circle axis were recognized by using edge detection and the geometrical characteristics of the pointer meter, and then, the scales and scale values were determined using the statistical characteristics of the circles around the pointer’s vertex. Next, by utilizing the density-based spatial clustering of applications with noise (DBSCAN) algorithm, we adjusted the scales and the scale values, taking full advantage of the pointer’s characteristic that the scales’ rotation angles changed linearly with the scale values, which were classified by an SVM taking in the HOG feature as the input. Finally, the indication was computed in terms of the relative position of the pointer between the adjacent scale values, and the accuracy was 94.2%. The proposed method was evaluated by comparing it with some newly published algorithms. The pointer detection method had stronger robustness and anti-interference capability than the image segmentation method and the skeleton extraction method. The Θ(n) value, indicating the complexity of the scale detection algorithm, was considerably smaller than the Θ(n²) value, denoting the modified central mapping method used mostly for scale detection. The energy consumption by each module was measured, and the results showed that the image acquisition module accounted for the largest energy consumption, 87.7%, which was more than eight times as much as the second-largest energy consumption, 10.3% (that of the wireless transmission module); the problem of power supply could be solved by using solar panels.

一些非数字化的测量仪器（例如指针式仪表）由于其高稳定性和准确性而被某些农田所利用，并且取决于人类运动的数据收集需要大量的时间和精力，并且技术水平较低。因此，提出了一种利用机器视觉和无线传感器网络（WSN）技术的智能指针表指示读取和自动收集方法。开发了一种由图像采集模块，智能控制中心，无线传输模块和电源模块组成的智能指针仪表读数装置，可以完成智能仪表读数的读取和数据的自动上传。此外，提出了一种用于资源受限环境的指针指示器指示读取算法。首先，通过边缘检测和指针测量仪的几何特征识别指针和圆轴，然后使用指针顶点周围的圆的统计特征确定比例和比例值。接下来，通过利用基于噪声的应用程序的基于密度的空间聚类（DBSCAN）算法，我们充分利用了指针的特性，即刻度尺的旋转角度随刻度值线性变化，从而调整了刻度和刻度值。由SVM分类，并以HOG功能作为输入。最后，根据指针在相邻刻度值之间的相对位置来计算指示，精度为94.2％。通过将其与一些新发布的算法进行比较，对提出的方法进行了评估。指针检测方法比图像分割方法和骨架提取方法具有更强的鲁棒性和抗干扰能力。表示规模检测算法复杂度的Θ（n）值比Θ（n²）值，表示主要用于比例尺检测的修改后的中心映射方法。测量每个模块的能耗，结果显示，图像采集模块占最大能耗，为87.7％，是第二大能耗的10.3％（是第二大能耗的8倍）。无线传输模块）；使用太阳能电池板可以解决电源问题。