A Novel System to Monitor Illegal Sand Mining using Contour Mapping and Color based Image Segmentation

摘要

Developing nations face the issue of illegal and excessive land mining which has adverse effects on the environment. A robust and cost effective system is presented in this paper to monitor the mining process. This system includes a novel vehicle detection approach for detecting vehicles from static images and calculating the amount of sand being carried to prevent the malpractices of sand smuggling. Different from traditional methods, which use machine learning to detect vehicles, this method introduces a new contour mapping model to find important “vehicle edges” for identifying vehicles The sand detection algorithm uses color based segmentation since sand can have various colors under different weather and lighting conditions The proposed new color segmentation model has excellent capabilities to identify sand pixels from background, even though the pixels are lighted under varying illuminations. The detected amount of sand is checked against the maximum set threshold value specific to the recognized vehicle. Experimental results show that the integration of Hough features and color based image segmentation is powerful. The average accuracy rate of the system is 94.9%.