基于注意力机制和编码-解码架构的施工场景图像描述方法

为了实现在光线不佳、夜间施工、远距离密集小目标等复杂施工场景下的图像描述，提出基于注意力机制和编码-解码架构的施工场景图像描述方法. 采用卷积神经网络构建编码器，提取施工图像中丰富的视觉特征；利用长短时记忆网络搭建解码器，捕捉句子内部单词之间的语义特征，学习图像特征与单词语义特征之间的映射关系；引入注意力机制，关注显著性强的特征，抑制非显著性特征，减少噪声信息的干扰. 为了验证所提方法的有效性，构建一个包含10种常见施工场景的图像描述数据集. 实验结果表明，所提方法取得了较高的精度，在光线不佳、夜间施工、远距离密集小目标等复杂施工场景下具有良好的图像描述性能，且具有较强的泛化性和适应性.

A image caption method of construction scene based on attention mechanism and encoding-decoding architecture

A construction scene image caption method based on attention mechanism and encoding-decoding architecture was proposed, in order to realize the image caption in the complex construction scenes such as poor light, night construction, long-distance dense small targets and so on. Convolutional neural network was used to construct encoder to extract rich visual features in construction images. Long short-term memory network was used to construct decoder to capture semantic features of words in sentences and learn mapping relationship between image features and semantic features of words. Attention mechanism was introduced to focus on significant features, suppress non-significant features and reduce interference of noise information. An image caption data set containing ten common construction scenes was constructed in order to verify the effectiveness of the proposed method. Experimental results show that the proposed method achieves high accuracy, has good image caption performance in complex construction scenes such as poor light, night construction, long-distance dense small targets and so on, and has strong generalization and adaptability.