SmokeFCM: Segmentation of Quarry Blast Smoke Plumes using Self-Adaptive Weighted Fuzzy C-Means

The segmentation of quarry blast smoke plumes is a crucial research area that has the potential to enhance safety, environmental monitoring, object detection, and regulatory compliance in the mining industry. Smoke plume segmentation can be used to identify the spatial extent and temporal evolution of smoke plumes generated by the blast. This information can be used to improve the understanding of blast dynamics and optimize blast designs. Furthermore, smoke plume segmentation can assist in identifying potential environmental and health hazards associated with the release of smoke and dust particles into the atmosphere during quarry blasts. However, many current smoke plume segmentation algorithms are designed for segmentation of smoke plumes from fires and are not suitable for segmentation of plumes from quarry blasts. The proposed method in this paper leverages the video frame captured before the blast as a stationary background image, which helps to reduce other confuser objects present in the image along with a selfadaptive clustering method. The color channels of the image are utilized based on the different contrast properties of the smoke, with a self-adaptive gradient compensation applied for edge detection of the smoke plume. This approach offers a novel and effective way to segment smoke in quarry blast analysis, with potential applications in the calculation of noxious gas volume and concentration within the plume. Compared to other algorithms, our method improves accuracy, decreases the false alarm rates, and reduces the overall miss rate. Overall, this paper contributes to the research on smoke plume image segmentation, particularly for quarry blast smoke plumes, which possess unique characteristics that require specialized algorithms for accurate segmentation.