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Recognition algorithm for laboratory protective equipment based on improved YOLOv7.
Huijuan Luo1,2, Wenjing Liu2, Pinghu Xu1
1National Center for Materials Service Safety, University of Science and Technology Beijing, Beijing, 100083, China.
Heliyon
|September 10, 2024
Summary
An improved YOLOv7 algorithm enhances detection of small personal protective equipment (PPE) targets in lab videos. This intelligent system boosts safety compliance by accurately identifying missing helmets, goggles, and masks in complex environments.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Laboratory Safety
Background:
- Laboratory personnel often neglect safety standards, failing to wear essential personal protective equipment (PPE).
- Manual inspection of PPE compliance is inefficient, costly, and prone to errors.
- Detecting small PPE items in video surveillance presents a significant challenge for existing algorithms.
Purpose of the Study:
- To develop an intelligent and efficient detection technology for laboratory personal protective equipment (PPE).
- To improve the accuracy and robustness of PPE recognition in complex laboratory settings.
- To address the limitations of current methods in detecting small targets within video surveillance.
Main Methods:
- An improved YOLOv7 algorithm was developed, incorporating a Global Attention Mechanism (GAM) into the Efficient Layer Aggregation Network (ELAN) for enhanced feature extraction (ELAN-G).
- The Normalized Gaussian Wasserstein Distance (NWD) metric was introduced to improve the detection of small targets, replacing the CIoU metric.
- A novel, multidimensional laboratory personal protective equipment (PPE) dataset was constructed to train and evaluate the algorithm.
Main Results:
- The improved YOLOv7 model achieved a mean Average Precision (mAP) of 84.2%, a 2.3% increase over the original model.
- The algorithm demonstrated a 5% improvement in detection rate and a 2% improvement in Micro-F1 score compared to the baseline.
- Experimental results showed marked accuracy enhancement compared to prevailing algorithms, particularly for small PPE targets in complex scenarios.
Conclusions:
- The proposed improved YOLOv7 algorithm effectively addresses the challenge of detecting small personal protective equipment (PPE) in complex laboratory environments.
- This intelligent detection system offers a robust solution for enhancing laboratory safety management and compliance.
- The developed method significantly improves the accuracy and efficiency of PPE recognition in real-world laboratory settings.
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