## 1 Introduction
The color information is not taken into account when extracting froth texture features using the wavelet transform and texture spectrum, and the texture is instead described by conventional statistics (such as uniformity, entropy, etc. ), which makes it difficult to completely and accurately represent the local differences in the froth image texture under various working conditions. The working conditions of the flotation process can also be reflected by the size features of the froth, which mainly include the average size and deflection of the froth [17]. Beginning with the accurate segmentation of the froth image, froth size features can be accurately acquired. In the past, researchers have conducted extensive research on froth segmentation methods, including threshold segmentation [18], watershed segmentation [19] and valley edge detection [20], three widely used froth image segmentation methods in the flotation process. According to the features of flotation froth images, researchers concluded that the watershed segmentation algorithm is suitable for segmenting mineral flotation froth images among many segmentation algorithms. However, the traditional watershed segmentation algorithm is prone to over-segmentation or under-segmentation. Therefore, researchers subsequently improved the watershed segmentation algorithm to promote the accuracy of flotation froth image segmentation and solved this problem to a certain extent [21; 22; 23; 24]. In order to accurately extract the size features of flotation froth, further improvements are needed in the segmentation of mineral flotation froth images. The size features of the froth image are extracted after the froth image segmentation of mineral flotation has been completed. In previous studies, when extracting the size features of froth images, most post-processing analyses only extracted single-valued features, such as the mean, variance and peak value of the froth size, while ignoring the overall distribution of the froth size. Since flotation froth contains a large amount of information reflecting flotation conditions, scholars, both domestically and internationally, have conducted a great number of studies on the detection of flotation process working conditions based on froth image features [25; 26]. [PERSON] et al. [27] proposed a flotation working condition recognition method using an LVQ neural network and a rough set on the basis of extracting froth texture features. [PERSON] et al. [28] proposed a fault detection method, which realizes the fault detection and diagnosis of the flotation process by detecting the filter based on the output probability density function and Lyapunov stability analysis. [PERSON] et al. [29] proposed a fault working condition identification method for the antimony flotation process based on multi-scale texture features and embedding prior knowledge of the K mean. By first extracting features from the flotation froth image and then feeding the generated feature vectors to the classifier, these techniques enable the recognition of the flotation process working conditions. The results demonstrate that these methods can identify the working conditions of the flotation process to a certain extent. However, excessive are included in the extracted features, which affects the accuracy of recognition. Deep learning networks can now automatically extract deep features from image data with better flexibility and classification ability, so researchers have begun to use deep learning networks to identify the working conditions of the flotation process [30; 31; 32]. [PERSON] et al. [33] used convolutional neural networks for pre-training on image databases of common objects for extracting features from flotation froth images and then effectively identified the working conditions of the mineral flotation process. [PERSON] et al. [34] studied convolutional neural networks to classify froth images collected from industrial coal flotation towers operating under various process working conditions. [PERSON] et al.