In response to challenges such as scale variation， background interference， and partial occlusion in crowd counting within complex scenes， a DA-DCCNN （Dual Attention based Dilated Contextual Convolutional Neural Network） was proposed. Firstly， the convolutional layers from VGG16 were utilized as feature extractors to obtain abstract and deep-level feature maps of the crowd image. Subsequently， by employing dilated convolutions， a Dilated Context Module （DCM） was constructed to connect features obtained from different layers. The Spatial Attention Module （SAM） and Channel Attention Module （CAM） were introduced to acquire contextual information. Finally， a loss function was formulated by combining the Euclidean distance and cross entropy to measure the disparity between the predicted attention map and the ground truth attention map. Experimental results on three publicly available datasets — ShanghaiTech， UCF_CC_50 and UCF-QNRF demonstrate that DA-DCCNN can effectively capture multi-scale features in the image and enhance the perception of important regions and channels within the image， achieving the optimal Mean Absolute Error （MAE）. The feature fusion network based on dual attention mechanism can efficiently recognize spatial structures and local features in images so that by using the generated density maps， the crowd regions can be predicted and counted more accurately.

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.