For concrete dam construction

traditional computer vision target recognition methods are difficult to meet the requirements for intelligent detection in complex construction sites

as it involves narrow spaces

continuous process transitions

and various other elements such as personnel

machinery

and environment (human-machine-environment or HME). These elements often lead to occlusions

dense overlaps

and variations in size and orientation. This paper describes a new method

YOLOv5-SS

for recognition of the multiple elements in the HME scenarios of such construction. By integrating a CBAM attention module

this method improves the performance of the object detector and enhances its sensitivity to HME elements of different sizes and positions. And

it incorporates the weighted bidirectional feature pyramid network (BiFPN) to enable the object detector to focus on key image information related to real-time HME elements. To validate the recognition capability of this method

a dataset based on image information from a concrete arch dam construction site is used. Comparison of YOLOv5-SS with the YOLOv5 and Faster R-CNN models demonstrates it effectively improves the efficiency and accuracy of target detection in concrete dam construction scenarios.