针对地铁站屏蔽门与列车门间隙空间异物检测问题，提出了一种结合语义分割 与背景参考的前景检测方法。该方法通过深度神经网络 DifferentNet 检测图像中的异物 区域，首先，在列车停靠站台的过程中采集背景图片和待检测图片，通过网络的编码部 分提取图像的特征信息得到特征金字塔，将两幅图片的特征图连接，再由解码部分计算 特征差异得到待检测图片的前景热力图，最后经阈值分割和轮廓筛选得到检测结果。实 验结果表明，该方法的前景交并比 ( c_IoU) 达 81.2% ，调和均值 F₁ 达 89.5%，运行速率 为 30 帧/s，与传统方法及无背景参考的图像分割网络相比，取得了更好的效果。

A foreground detection method based on semantic segmentation and background reference was proposed to solve the problem of foreign objects detection in the space between platform screen doors and train doors in metro stations． This method used a depth neural network—DifferentNet to detect foreign objects region in images． Firstly， the background image and the image to be detected were obtained during a metro stop at the platform． The feature pyramid was obtained by extracting the feature information of the images through the encoding part of the network， and feature maps of the two images were merged by concatenation． Then the foreground heat map of the image to be detected was obtained by calculating the feature difference in the decoding part． Finally，the heat map was thresholded and filtered to get detection results． The results show that this method can achieve a high foreground IoU of 81. 2% and F1-score of 89. 5% ． Furthermore，it reached 30 fps in speed． The proposed method performed better than traditional methods and other image segmentation networks without background reference．