A high-performance framework for personal protective equipment detection on the offshore drilling platform

Abstract

In order for the offshore drilling platform to operate properly, workers need to perform regular maintenance on the platform equipment, but the complex working environment exposes workers to hazards. During inspection and maintenance, the use of personal protective equipment (PPE) such as helmets and workwear can effectively reduce the probability of worker injuries. Existing PPE detection methods are mostly for construction sites and only detect whether helmets are worn or not. This paper proposes a high-precision and high-speed PPE detection method for the offshore drilling platform based on object detection and classification. As a first step, we develop a modified YOLOv4 (named RFA-YOLO)-based object detection model for improving localization and recognition for people, helmets, and workwear. On the basis of the class and coordinates of the object detection output, this paper proposes a method for constructing position features based on the object bounding box to obtain feature vectors characterizing the relative offsets between objects. Then, the classifier is obtained by training a dataset consisting of position features through a random forest algorithm, with parameter optimization. As a final step, the PPE detection is achieved by analyzing the information output from the classifier through an inference mechanism. To evaluate the proposed method, we construct the offshore drilling platform dataset (ODPD) and conduct comparative experiments with other methods. The experimental results show that the method in this paper achieves 13 FPS as well as 93.1% accuracy. Compared to other state-of-the-art models, the proposed PPE detection method performs better on ODPD. The method in this paper can rapidly and accurately identify workers who are not wearing helmets or workwear on the offshore drilling platform, and an intelligent video surveillance system based on this model has been implemented.

Deep Learning for Detection of Proper Utilization and Adequacy of Personal Protective Equipment in Manufacturing Teaching Laboratories

“…Apart from construction sites, the detection of PPE objects is also performed in offshore drilling operations. A study conducted by [17] aimed to propose a framework for PPE detection. The proposed framework aimed to enhance the accuracy, reliability, and performance of PPE detection compared to existing methods.…”

“…Meanwhile, research by [9], Refs. [14,15] conducted in industrial settings, and [17] conducted research in the offshore drilling environment. The common objective of these previous studies was to detect PPE to prevent workplace accidents.…”

MEAG-YOLO: A Novel Approach for the Accurate Detection of Personal Protective Equipment in Substations

“…However, it struggles to detect small objects. Ji et al [27] improved the YOLOv4 model by integrating a residual feature enhancement network. This network preserves critical information in high-level feature maps, enhancing object detection accuracy.…”

GBSG-YOLOv8n: A Model for Enhanced Personal Protective Equipment Detection in Industrial Environments

“…Jiang et al [35] enhanced YOLOv3 by incorporating squeezeand-excitation (SE) blocks between convolution layers in Darknet53, substituting the mean squared error (MSE) with GIoU loss, and employing focal loss to mitigate the significant foreground-background class imbalance issue, thereby more effectively achieving the realtime monitoring of mask-wearing. Ji et al [36] introduced a residual feature enhancement module based on YOLOv4, reducing the loss of valuable information in high-level feature maps, enhancing object detection accuracy, and enabling the timely detection of workers who not wearing safety helmets or clothing in industrial environments. Wang et al [37] tested the performance of YOLOv3, YOLOv4, and YOLOv5 on a custom dataset.…”