基于煤矿井下传送带空载检测

煤矿井下传送带在无煤或煤量较少的状态下长时间高速运转,会耗费大量的电能.为了节省井下传送带造成的电能损耗,本文提出了一种边缘结构相似算法和YOLOv3结合的传送带空载判定方法.通过边缘结构相似算法将结构特征和边缘特征相融合,每相邻10帧比较图片的相似度,连续比较3次判断传送带的运行状态.若传送带运行,则运用自适应锚框机制的YOLOv3模型,检测传送带上的煤量,最后判断传送带是否空载.实验结果表明,该方法可以有效准确的判断传送带的空载状态,检测准确率达到96.85%.

No-Load Detection of Underground Conveyor Belt in Coal Mine

The long-time and high-speed running of an underground conveyor belt in a coal mine will consume a lot of electricity in the case of no coal or little coal. In order to reduce the electricity loss caused by the underground conveyor belts, we propose a no-load determination method of the conveyor belts combining the YOLOv3 algorithm with the edge-based structural similarity algorithm. First, the structure features and edge features are fused by the edge-based structural similarity algorithm. Then, the similarity of adjacent 10 frames of images is successively compared three times to judge the running state of a conveyor belt. If the conveyor belt is running, the YOLOv3 model based on the adaptive anchor box mechanism is used to detect the coal amount on the conveyor belt. Finally, whether the conveyor belt carries a load or not is judged. The experimental results show that the proposed method can effectively and accurately judge the no-load state of the conveyor belts and the detection accuracy reaches 96.85%.