期刊文献+
共找到2篇文章
< 1 >
每页显示 20 50 100
Developing selective mining capability for longwall shearers using thermal infrared-based seam tracking 被引量:14
1
作者 Jonathon C. Ralston Andrew D. Strange 《International Journal of Mining Science and Technology》 SCIE EI 2013年第1期47-53,共7页
Longwall mining continues to remain the most efficient method for underground coal recovery. A key aspect in achieving safe and productive longwall mining is to ensure that the shearer is always correctly positioned w... Longwall mining continues to remain the most efficient method for underground coal recovery. A key aspect in achieving safe and productive longwall mining is to ensure that the shearer is always correctly positioned within the coal seam. At present, this machine positioning task is the role of longwall personnel who must simultaneously monitor the longwall coal face and the shearer's cutting drum position to infer the geological trends of the coal seam. This is a labour intensive task which has negative impacts on the consistency and quality of coal production. As a solution to this problem, this paper presents a sensing method to automatically track geological coal seam features on the longwall face, known as marker bands, using thermal infrared imaging. These non-visible marker bands are geological features that link strongly to the horizontal trends present in layered coal seams. Tracking these line-like features allows the generation of a vertical datum that can be used to maintain the shearer in a position for optimal coal extraction. Details on the theory of thermal infrared imaging are given, as well as practical aspects associated with machine-based implementation underground. The feature detection and tracking tasks are given with real measurements to demonstrate the efficacy of the approach. The outcome is important as it represents a new selective mining capability to help address a long-standing limitation in longwall mining operations. 展开更多
关键词 Selective mining Longwall shearer Horizon control Thermal infrared tracking
在线阅读 下载PDF
A Comprehensive Method to Reject Detection Outliers by Combining Template Descriptor with Sparse 3D Point Clouds
2
作者 郭立 《Journal of Shanghai Jiaotong university(Science)》 EI 2017年第2期188-192,共5页
We are using a template descriptor on the image in order to try and find the object. However, we have a sparse 3D point clouds of the world that is not used at all when looking for the object in the images. Considerin... We are using a template descriptor on the image in order to try and find the object. However, we have a sparse 3D point clouds of the world that is not used at all when looking for the object in the images. Considering there are many false alarms during the detection, we are interested in exploring how to combine the detections on the image with the 3D point clouds in order to reject some detection outliers. In this experiment we use semi-direct-monocular visual odometry (SVO) to provide 3D points coordinates and camera poses to project 3D points to 2D image coordinates. By un-projecting points in the tracking on the selection tree (TST) detection box back to 3D space, we can use 3D Gaussian ellipsoid fitting to determine object scales. By ruling out different scales of detected objects, we can reject most of the detection outliers of the object. © 2017, Shanghai Jiaotong University and Springer-Verlag Berlin Heidelberg. 展开更多
关键词 semi-direct-monocular visual odometry(SVO) tracking on the selection tree(TST)-recognizer 3D point-clouds Gaussian ellipsoid fitting
原文传递
上一页 1 下一页 到第
使用帮助 返回顶部