Target Detection of Safety Protective Gear Using the Improved YOLOv5

TL;DR

This paper introduces YOLO-EA, an improved real-time object detection model that enhances safety gear detection in railway construction by integrating ECA and EIoU loss, achieving higher accuracy and efficiency.

Contribution

The paper presents YOLO-EA, a novel model that improves small object detection and occlusion handling in safety gear monitoring, outperforming YOLOv5 in accuracy and speed.

Findings

Achieves 98.9% precision and 94.7% recall on real-world data.

Maintains real-time detection at 70.774 fps.

Outperforms YOLOv5 in accuracy and robustness.

Abstract

In high-risk railway construction, personal protective equipment monitoring is critical but challenging due to small and frequently obstructed targets. We propose YOLO-EA, an innovative model that enhances safety measure detection by integrating ECA into its backbone's convolutional layers, improving discernment of minuscule objects like hardhats. YOLO-EA further refines target recognition under occlusion by replacing GIoU with EIoU loss. YOLO-EA's effectiveness was empirically substantiated using a dataset derived from real-world railway construction site surveillance footage. It outperforms YOLOv5, achieving 98.9% precision and 94.7% recall, up 2.5% and 0.5% respectively, while maintaining real-time performance at 70.774 fps. This highly efficient and precise YOLO-EA holds great promise for practical application in intricate construction scenarios, enforcing stringent safety compliance during complex railway construction projects.