In this paper, we proposed a semantic fire image segmentation method using a convolutional neural network. The simple but powerful method proposed is middle skip connection achieved through the residual network, which is widely used in image-based deep learning. To enhance the middle skip connection, we constructed a pair of convolution layers, hereafter referred to as input convolution and output convolution, to be inserted in front and behind of the entire architecture. Consequently, the middle skip connection yields a stronger feedback effect compared to when only the short skip connection of the residual block and the long skip connection are used. The validity of the proposed method has been confirmed by using the FiSmo dataset and the Corsican Fire Database based on various evaluation metrics. Comparative analysis shows that the proposed model outperforms previous fire segmentation deep learning models and image processing algorithms.

在本文中，我们提出了一种使用卷积神经网络的语义火灾图像分割方法。提出的一种简单但功能强大的方法是通过残差网络实现中间跳过连接，该方法广泛应用于基于图像的深度学习中。为了增强中间跳过连接，我们构造了一对卷积层，以下称为输入卷积和输出卷积，将其插入到整个体系结构的前面和后面。因此，与仅使用剩余块的短跳跃连接和长跳跃连接时相比，中间跳跃连接产生更强的反馈效果。通过使用FiSmo数据集和基于各种评估指标的Corsican Fire数据库，已证实了该方法的有效性。