针对在线选煤过程中，光照条件不足引起煤矸表面特征成像质量差，煤矸识别率低下的问题，提出一种融合光源分布、色温、光照强度等多因素条件的新型高质量煤矸图像获取方法。首先，针对煤矸流实际洗选过程中光照条件的复杂性导致的光照不均匀问题，基于九点取样法研究了不同入射角度对光照均匀度的影响，确定适用于煤流的最佳入射角度。然后，针对不同色温引起色彩还原性不同导致煤矸图像失真的问题，采用MSE、PSNR和SSIM等指标量化分析多样本单一种类煤矸图像失真情况，通过TOPSIS算法研究不同色温的光源对煤、矸石，以及混合煤矸石3种不同工况图像失真情况的影响，确定成像质量最优的色温。最后，考虑在线光照强度变化影响煤矸表面特征信息的表达，基于不同照度下煤矸表面特征响应曲线，建立曝光时间、输送带速度和环境光强的关联关系，量化表面区分度较大的照度区间，确定最佳光照条件。通过融合多因素光照条件下高质量图像获取方法建立煤矸识别系统，并对SSD和Faster-RCNN目标检测模型进行实验验证。结果表明：该方法在很大程度上提高了煤矸图像质量，煤矸识别准确率提高10.5%，为煤矸智能分选系统提供更为准确的数据支撑，对提高原煤入选率具有一定应用推广价值。

Aiming at the problem of poor imaging quality of coal and gangue surface features and low identification rate of coal and gangue caused by insufficient illumination conditions in the process of online coal separation,a new high-quality coal gangue image acquisition that integrates multiple factors such as light source distribution, color temperature, and light intensity is proposed. First of all, in view of the uneven illumination caused by the complexity of the illumination conditions in the actual washing process of coal gangue, the influence of different incident angles on the uniformity of illumination is studied based on the nine-point sampling method, and the best incident angle suitable for coal flow is determined. Then, in view of the problem of coal gangue image distortion caused by different color temperatures caused by different color reproducibility, indicators such as MSE, PSNR, and SSIM are used to quantitatively analyze the image distortion of multi-sample single type coal gangue, and the TOPSIS algorithm is used to study the effect of light sources with different color temperatures on coal and gangue. And the influence of the image distortion of the three different working conditions of mixed coal gangue to determine the color temperature with the best image quality. Finally, considering that online light intensity changes affect the expression of coal gangue surface feature information, based on the response curve of coal gangue surface characteristics under different illuminances, the correlation relationship between exposure time, belt speed and ambient light intensity is established to quantify the illumin- ance interval with greater surface discrimination.and determine the optimal light conditions. The coal gangue recognition system is established by fusion of high-quality image acquisition methods under multi-factor lighting conditions, and the SSD and Faster-RCNN target detection models are experimentally verified. The results show that this method has greatly improved the image quality of coal gangue, and the accuracy of coal gangue identification has been increased by 10.5%, providing more accurate data support for the intelligent coal gangue sorting system, and has certain application and promotion value for improving the raw coal selection rate.

0 引言
1 多因素光照条件高质量图像获取方法
2 煤矸识别光源分析
2.1 煤矸石识别光源最优布置
2.2 煤矸识别色温分析
2.3 煤矸识别光照强度分析
3 试验与结果分析
3.1 图像采集装置
3.2 高质量图像获取方法验证
3.3 煤矸识别测试试验
4 结论