Image Feature Based Smoke Recognition in Mines Using Monocular Camera Mounted on Aerial Vehicles
Conference
Nagaraj, K, Sadashiva, TG, Ramani, SK et al. (2018). Image Feature Based Smoke Recognition in Mines Using Monocular Camera Mounted on Aerial Vehicles
. 10.1109/ICECIT.2017.8453281
Nagaraj, K, Sadashiva, TG, Ramani, SK et al. (2018). Image Feature Based Smoke Recognition in Mines Using Monocular Camera Mounted on Aerial Vehicles
. 10.1109/ICECIT.2017.8453281
Fire and smoke are common sight at mines and could be catastrophic for humans working there. Our paper focuses on an alert mechanism that is built based on detection and validation of smoke from mines. We propose a computer vision based model that is robust enough to detect presence of smoke and the direction of motion of smoke. This research paper proposes a smoke detection model integrated with Unmanned Aerial Vehicle (UAV). The model focuses on smoke detection that leads to early detection of fire. The model works on the basis of image processing. The distinguishing feature of our model is the dynamic and real time processing. Here the system tracks the dynamic environment in the mining areas using UAVs featured with cameras. The real time captured video frames are calibrated to overcome distortion considering the radial and tangential factors. The calibrated frame thus obtained is further processed to get the Region of Interest (ROI) based on the spatial Hue Saturation Value (HSV) colouring model. Each pixel of the frame is then compared with colour range of smoke using opencv functions. The matched ROI is de-noised using Non-local means De-nosing algorithm to avoid unnecessary colours. These filtered frames are then processed concurrently to identify the pattern of change in motion. Based on this pattern the ROI is classified as either smoke or no smoke. With the analysis of turbulence in each pixel the intensity of smoke is detected. The experiment is conducted with-1) Prerecorded smoke video clippings. 2) Real time video captured in the camera. The results turned out to be positive in both the test cases. So, the model works quite efficiently with both recorded and real time video. Key Terms: UAV, ROI, HSV, De-noising algorithm, pattern matching.
