Intelligent driving system at opencast mines during foggy weather. Issue 3 (16th March 2022)

• Record Type: Journal Article
• Authors: Kumari, Sushma; Choudhary, Monika; Kumari, Khushboo; Kumar, Virendra; Chowdhury, Abhishek; Chaulya, Swades Kumar; Prasad, Girendra Mohan; Mandal, Sujit Kumar
• Is Part Of: International journal of mining, reclamation and environment. Volume 36:Issue 3(2022)
• Page Start: 196
• Page End: 217
• Publication Date: 2022-03-16
• Subjects: Foggy weather -- driving assistant -- video stitching -- image processing -- object detection -- convolution neural network; Mining engineering -- Periodicals; Mineral industries -- Environmental aspects -- Periodicals; Abandoned mined lands reclamation -- Periodicals; 622.292
• DOI: 10.1080/17480930.2021.2009724
• Languages: English
• ISSNs: 1748-0930

Abstract

ABSTRACT: The fog in mining operations minimises the visibility, preventing drivers from a clear view, causing accidents and vehicle collisions. This paper provides an intelligent driving system for heavy earthmoving machinery operators in opencast mines, including hardware and software. Hardware contains high definition and thermal cameras, a global navigation satellite system (GNSS), radar, laser light, wireless devices, graphical processing unit, touch screen, etc. The software covers image stitching, image enhancement, and convolution neural network-based object detection. The display dashboard is divided into four windows. Each window represents a different view, i.e. 180° panorama view of the driving lane, GNSS tracking map, proximity radar detection view, and rear thermal camera view. An additional colour transfer method has been used in the existing image stitching method to reduce misalignment and ghost effect in the panorama output. The proposed method outperformed the existing methods, namely contrast limited adaptive histogram equalisation (CLAHE) and dark channel prior (DCP). The proposed image enhancement technique has increased contrast, entropy, and colour average by 0.069, 0.43, and 13.96, respectively, than CLAHE, and 0.994, 0.43, and 42.07 than DCP. The accuracy of the object detection model is 97%, and the overall processing time of all the algorithms is 0.44949 seconds.