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.

利用全局稳定优化YOLO模型(GSO-YOLO)解决复杂施工环境中的安全问题，并通过整合全局优化模块(GOM)和稳定捕获模块(SCM)以提高全局上下文信息的捕获和检测稳定性，以及创新的AIoU损失函数来提高检测精度和效率。实验结果表明，GSO-YOLO在SODA、MOCS和CIS等数据集上表现优于现有方法，达到了SOTA水平。

GSO-YOLO: 建筑工地检测的全局稳定优化YOLO