基于机器视觉的煤中杂物智能分选系统研究

A study of the machine vision-based intelligent separation system for extraction of tramp materials in raw coal

为解决煤炭分选过程中杂物对生产的影响及由此产生的各种问题,设计了基于深度学习和机器视觉的集煤中杂物智能识别、定位和分拣于一体的杂物智能分选系统。该系统建立了基于语义分割的像素级杂物识别模型,计算成本比标准卷积网络模型降低8~9倍;构建了复杂环境条件下的机械手精准抓取策略,能够避开干扰物,实现硬质物料、轻质物料抓取点的精确选择。在涡北选煤厂的应用测试表明,该系统杂物检测准确率为96.647%,机械手分拣成功率为94.759%,系统分拣率为91.640%,能够高效除去煤中杂物,提高了杂物分选过程的智能化水平。

In order to eliminate the adverse effect on and safety risks in coal cleaning operations produced by presence of tramp materials in raw coal, a smart tramp material recognition-localization-separation integrated system based on deep learning and machine vision is developed. A pixel-level recognition model based on semantic segmentation is likewise developed with its computional costs 8~9 times less than that of the standard convolution network model. The system is provided with a robotic arm which is capable of making recognition and selection of points for gripping of hard and light foreign materials and keeping clear of any interference, in an accurate manner, even under complex working environmental conditions. Result of test conducted with the system at Guobei Coal Preparation Plant shows that the system can work with a recognition rate up to 96.647%, an extraction success rate as high as 94.759% and a sorting rate of 91.640%. It well demonstrates that the use of the system can realize high-efficiency removal of foreign matters and lead to enhancement of the intelligent level in this respect.