YOLOv5s-gⁿConv: detecting personal protective equipment for workers at height

Falls from height (FFH) accidents can devastate families and individuals. Currently, the best way to prevent falls from heights is to wear personal protective equipment (PPE). However, traditional manual checking methods for safety hazards are inefficient and difficult to detect and eliminate potential risks.

To better detect whether a person working at height is wearing PPE or not, this paper first applies field research and Python crawling techniques to create a dataset of people working at height, extends the dataset to 10,000 images through data enhancement (brightness, rotation, blurring, and Moica), and categorizes the dataset into a training set, a validation set, and a test set according to the ratio of 7:2:1. In this study, three improved YOLOv5s models are proposed for detecting PPE in construction sites with many open-air operations, complex construction scenarios, and frequent personnel changes. Among them, YOLOv5s-gnconv is wholly based on the convolutional structure, which achieves effective modeling of higher-order spatial interactions through gated convolution (gnConv) and cyclic design, improves the performance of the algorithm, and increases the expressiveness of the model while reducing the network parameters.

Experimental results show that YOLOv5s-gnconv outperforms the official model YOLOv5s by 5.01%, 4.72%, and 4.26% in precision, recall, and mAP_0.5, respectively. It better ensures the safety of workers working at height.

To deploy the YOLOv5s-gnConv model in a construction site environment and to effectively monitor and manage the safety of workers at height, we also discuss the impacts and potential limitations of lighting conditions, camera angles, and worker movement patterns.