  Safety issues at construction sites have long plagued the industry, posing
risks to worker safety and causing economic damage due to potential hazards.
With the advancement of artificial intelligence, particularly in the field of
computer vision, the automation of safety monitoring on construction sites has
emerged as a solution to this longstanding issue. Despite achieving impressive
performance, advanced object detection methods like YOLOv8 still face
challenges in handling the complex conditions found at construction sites. To
solve these problems, this study presents the Global Stability Optimization
YOLO (GSO-YOLO) model to address challenges in complex construction sites. The
model integrates the Global Optimization Module (GOM) and Steady Capture Module
(SCM) to enhance global contextual information capture and detection stability.
The innovative AIoU loss function, which combines CIoU and EIoU, improves
detection accuracy and efficiency. Experiments on datasets like SODA, MOCS, and
CIS show that GSO-YOLO outperforms existing methods, achieving SOTA
performance.
