基于FCN-LSTM的工业烟尘图像分割

工业生产中常根据林格曼烟气黑度判断工业烟尘的污染等级，一种有效的方式是应用计算机视觉系统对工业烟尘进行监测， 其中对烟尘目标进行准确分割是该系统的关键技术。因为工业烟尘具有形状不固定、和云相似度高等特点，现有算法在复杂场景下对烟尘进行分割时容易受到干扰，分割准确度有待提高。针对这一问题，提出一种基于FCN-LSTM的工业烟尘图像分割方法，在全卷积网络对图像空间特征提取的基础上，使用长短时记忆网络提取图像序列的时间信息，通过烟尘的动态特征对运动的烟尘和背景进行区分，增强复杂场景下的抗干扰能力。实验表明，本文模型相比于全卷积网络，在复杂场景下的抗干扰能力有显著提升，能够有效克服来自云的干扰，对全卷积网络分割结果中易出现干扰点的问题也有改善，IoU指标最高有8.04%的提升。

An industrial smoke image segmentation method based on FCN-LSTM

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%.