GSO-YOLO: Global Stability Optimization YOLO for Construction Site Detection

TL;DR

This paper introduces GSO-YOLO, an improved object detection model tailored for construction site safety monitoring, which enhances detection stability and accuracy in complex environments through novel modules and loss functions.

Contribution

The study proposes GSO-YOLO, integrating GOM and SCM modules and an AIoU loss, to improve detection stability and accuracy in construction site scenarios.

Findings

GSO-YOLO outperforms existing methods on multiple datasets.

The model achieves state-of-the-art detection accuracy.

Enhanced stability and contextual understanding in complex scenes.

Abstract

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.