Abstract Opencast mining operations at hilly areas are usually affected during foggy weather due to the inability of drivers to operate heavy earth‐moving machinery in low visibility conditions. This article deals with an intelligent vision enhancement system for continuing opencast mining operations during foggy weather. The system integrates hardware and software to provide multistage safety features that make it unique from existing systems. The system includes hardware like thermal cameras, high definition cameras, proximity radar, wireless devices, GNSS module, graphical processing unit, display unit, and so forth, and image processing software, namely real‐time image stitching, image enhancement, and object detection using convolutional neural networks. The integrated system and algorithms display a 180° panorama field view of the vehicle's front using real‐time video stitching. The front view after image processing, rear camera view, object detection through proximity radar, and real‐time location of the vehicles on a 3D geo‐tagged mine map by GNSS modules are displayed in four splitter windows on a touch screen fitted on the dashboard in front of the driver's seat. The driver can drive the vehicle by seeing the display screen during foggy weather. The output image of the developed image‐processing algorithm has less distortion, better quality, and better depth perception than existing methods. Overall, there are significant improvements in the persistence of the color elements by 39.65%, contrast by 4.62%, and the corresponding entropy by 7.11% concerning the similar existing methods. The final system has been successfully tested in an opencast mine.

High dimensional data analysis gives rise to many challenges. One such that has come to gain a lot of attention recently is the concentration of distances (CoD) phenomenon, which is the inability of distance functions to distinguish points well in high dimensions. CoD affects almost every machine learning and data analysis algorithm in high dimensions. In this work, we present a novel efficient and effective empirical index that not only illustrates whether a distance function tends to concentrate for a given data set, but also enables us to measure the rate of concentration and allows us to compare different distance functions vis-a-vis their rate of concentration. As opposed to existing empirical indices, the proposed empirical measure uses only the internal characteristics of a given data set and hence is applicable on real data sets, which was hitherto not possible.