基于改进Mask R-CNN的建筑工地实例分割算法

实例分割对排除建筑工地不规则机械设备带来的安全隐患以及监测工人具有重要意义。然而当前主流的实例分割模型存在着边界检测精度不高的问题。结合实例分割的特点，提出了一种基于全局上下文通道注意力（GCCA）机制多阶段细化掩码的改进Mask R-CNN模型。首先，在Mask头部以多阶段的方式逐步融合细粒度特征，细化高质量掩码。其次，为了更好的融合细粒度特征，构建了GCCA注意力机制，其通过简化的全局上下文模块聚合全局特征，并利用一维卷积实现无降维的局部通道交互。实验结果表明，在COCO和MOCS数据集上均取得了较好的效果。其中，相较于Mask R-CNN模型，此算法在检测和分割的平均精度分别提高了2.4%和7.6%。

Building site instance segmentation algorithm based on improved Mask R-CNN

Instance segmentation is of great significance for eliminating safety hazards brought by irregular machinery and equipment on construction sites and for monitoring workers. However, the current mainstream instance segmentation models have the problem of low boundary detection accuracy. Combining the characteristics of instance segmentation, this paper proposes an improved Mask R-CNN model of multi-stage refining mask based on the global context channel attention (GCCA) mechanism. First, this paper gradually fuses fine grained features in the mask head in a multi-stage manner to refine high quality masks. Second, in order to better fuse fine-grained features, a GCCA attention mechanism is constructed, which aggregates global features through a simplified global context module, and utilizes one dimensional convolution to achieve local channel interactions without dimensionality reduction. The experimental results show that this paper has achieved great results on both COCO and MOCS datasets. Among them, compared with the Mask R-CNN model, the average accuracy of the algorithm in this paper in detection and segmentation is improved by 2.4% and 7.6% respectively.