A real-time object detection algorithm YOLO-C for complex construction environment was proposed for the problems of cluttered environment， obscured objects， large object scale range， unbalanced positive and negative samples， and insufficient real-time of existing detection algorithms， which commonly exist in construction environment. The extracted low-level features were fused with the high-level features to enhance the global sensing capability of the network， and a small object detection layer was designed to improve the detection accuracy of the algorithm for objects of different scales. A Channel-Spatial Attention （CSA） module was designed to enhance the object features and suppress the background features. In the loss function part， VariFocal Loss was used to calculate the classification loss to solve the problem of positive and negative sample imbalance. GhostConv was used as the basic convolutional block to construct the GCSP （Ghost Cross Stage Partial） structure to reduce the number of parameters and the amount of computation. For complex construction environments， a concrete construction site object detection dataset was constructed， and comparison experiments for various algorithms were conducted on the constructed dataset. Experimental results demonstrate that the YOLO?C has higher detection accuracy and smaller parameters， making it more suitable for object detection tasks in complex construction environments.

In the 3D point cloud semantic segmentation algorithm based on deep learning， to enhance the fine-grained ability to extract local features and learn the long-range dependencies between different local neighborhoods， a neural network based on attention mechanism and global feature optimization was proposed. First， a Single-Channel Attention （SCA） module and a Point Attention （PA） module were designed in the form of additive attention. The former strengthened the resolution of local features by adaptively adjusting the features of each point in a single channel， and the latter adjusted the importance of the single-point feature vector to suppress useless features and reduce feature redundancy. Second， a Global Feature Aggregation （GFA） module was added to aggregate local neighborhood features to capture global context information， thereby improving semantic segmentation accuracy. The experimental results show that the proposed network improves the mean Intersection?over?Union （mIoU） by 1.8 percentage points compared with RandLA-Net （Random sampling and an effective Local feature Aggregator Network） on the point cloud dataset S3DIS， and has good segmentation performance and good adaptability.