Conveyor belt tear detection method based on lion group optimization two-dimensional Otsu algorithm
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摘要: 基于传统二维Otsu分割算法的输送带撕裂检测方法采用穷尽搜索式的阈值选取方式，图像分割时间长、实时性差，不能同时满足撕裂检测精度与速度要求。针对上述问题，提出了一种基于狮群优化二维Otsu算法的输送带撕裂检测方法。首先通过输送带撕裂检测装置采集输送带图像，采用中值滤波和直方图均衡化对采集到的图像进行去噪和增强处理，突出撕裂部位；然后采用狮群优化二维Otsu算法对预处理过的图像求取接近实际的分割阈值，用该阈值对输送带图像进行分割处理；最后通过计算分割后图像中黑色像素点的数量进行撕裂诊断。仿真结果表明，该方法比基于传统二维Otsu算法的检测方法寻优能力更强，输送带分割效果更好，可以准确地从输送带图像中分割出裂痕，撕裂识别的正确率为98.2%，提高效率的同时缩短了运行时间，可以满足输送带撕裂检测的准确性和实时性要求。
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关键词:
- 输送带 /
- 撕裂检测 /
- 图像分割 /
- 分割阈值选取 /
- 狮群优化二维Otsu算法
Abstract: The conveyor belt tear detection methods based on traditional two-dimensional Otsu segmentation algorithm adopts threshold selection method of exhaustive search. The image segmentation has a long time and poor real-time performance, which cannot meet requirements of tear detection accuracy and speed at the same time. For the above problems, a conveyor belt tear detection method based on lion group optimization two-dimensional Otsu was proposed. Firstly, the conveyor belt image is collected by the conveyor belt tear detection device, median filtering and histogram equalization were used to denoise and enhance the collected image to highlight the torn part. Then, the close to actual segmentation threshold of preprocessed image is obtained by lion group optimization two-dimensional Otsu algorithm, and the conveyor image is segmented by this threshold. Finally, the tear diagnosis is performed by calculating the number of black pixels in the segmented image. The simulation results show that the optimization ability of proposed method is more powerful than the traditional two-dimensional Otsu algorithm, and segmentation effect of conveyor belt is better, the cracks can be accurately segmented from the conveyor belt image. The correct rate of tear recognition of the method is 98.2%, improves the efficiency and shortens the running time, which can meet the accuracy and real-time requirements of the conveyor belt tear detection. -
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