Abstract: In industrial production, the pollution level of industrial smoke and dust is often judged based on Ringelmann scale. An effective method is to monitor the industrial smoke using computer vision system. The accurate segmentation of smoke targets is the key to this system. Since the shape of industrial smoke is variable and similar to cloud, the existing algorithms do not work well in complex scenes, so the accuracy of segmentation needs to be improved. Aiming at this problem, this paper proposes an industrial smoke image segmentation method based on FCN-LSTM. On the basis of using fully convolutional network (FCN) to extract spatial features of the image, the time information of the image sequence is extracted by long short-term memory network (LSTM). The dynamic features of smoke and dust are used to distinguish the moving smoke and background, so as to enhance the anti-interference ability in complex scenes. Experiments show that, compared with the FCN, the proposed model can significantly improve the anti-interference ability in complex scenes. The model can effectively overcome the interference from the cloud, and solve the problem of interference points in the segmentation results of FCN. The IoU indicator is increased by up to 8.04%.

Key words: industrial smoke detection;image segmentation;fully convolutional network, long short-term memory network