# A New Smoke Segmentation Method Based on Improved Adaptive Density Peak Clustering
## 1 Introduction  
Image-based fire detection technology mainly includes key technologies such as fire image segmentation, feature extraction, fire judgment, and automatic fire extinguishing linkage. Fire image segmentation is the premise of fire feature extraction and recognition, and the segmentation result directly affects the accuracy of fire recognition. Therefore, the research of fire image segmentation technology is of great significance [1]. Popular fire segmentation algorithms include unsupervised fire smoke segmentation methods for infrared video [2; 3], fire segmentation methods based on multi-feature fusion [4], and fire segmentation based on convolutional neural networks [5; 6; 7; 8; 9], among other methods. For example, literature [3] proposed an image-processing-based forest fire detection using infrared cameras; literature [4] proposed a mining fire warning model based on patrol robots and multi-sensor and image recognition; and literature [5] proposed a convolutional neural network (CNN)-based smoke detection and segmentation framework. These methods cover different data sources as well as different feature analysis approaches and have been widely used in smoke detection. In the early stages of a fire outbreak, the smoke provides more timely potential information than flames [10]. Separating the target area from the background in the fire image is helpful for judging the location and hazard degree of the fire. Classical segmentation algorithms include threshold-based [11; 12], region-based [13; 14], and edge detection-based [15] segmentation algorithms. The threshold-based segmentation method usually only uses the pixel grayscale information of the image, does not consider the spatial correlation information between pixels, is easily affected by noise, and has pooranti-interference performance. Region-based segmentation methods tend to over-segment images. The segmentation method based on edge detection is simple and the detection speed is fast, but it is very sensitive to noise, so there will be problems such as edge blurring. Compared with the traditional image segmentation algorithm, the high-efficiency cluster segmentation algorithm has the advantages of unsupervised learning and has achieved better results in image segmentation. [PERSON] et al. (2016) propose a new method for identifying soot aggregates in transmission electron microscopy images using k-means clustering. They employ background removal, denoising, and image texture measurement to preprocess the image, and then perform segmentation using rolling ball transform and K-means. However, this method has many processes and complicated technology. [PERSON] et al. (2017) propose a novel neutrosophic image segmentation based on an improved fuzzy c-means algorithm (NIS-IFCM). They combine particle swarm optimization (PSO) and fuzzy c-means (FCM) to effectively remove image noise and make the boundaries of the segmented regions clearer. [PERSON] et al. (2018) propose CSFCM: an improved fuzzy c-means image segmentation algorithm using a collaborative approach. They use neural networks and Xie and Beni indices to determine the number of clustering centers, introduce a new meta-heuristic collaborative method to automatically determine the most suitable meta-heuristic parameters, and initialize the clustering centers. [PERSON] et al. (2019) propose CGFFCM: clustering weights and group local feature-weight learning in a fuzzy c-means clustering algorithm for color image segmentation. They used an automatic cluster weighting scheme to reduce the sensitivity to initialization and adopted a group local feature weighting strategy to achieve better image segmentation. [PERSON] et al. (2019) propose an improved intuitionistic fuzzy c-means for ship segmentation in infrared images. They incorporate target probability information into intuitionistic FCM, using a regularized form of neighborhood information to preserve image details.