Research on Small Target Detection Method for Industrial Safety Helmets Based on Improved YOLOv8

Industrial safety helmets are crucial personal protec-tive gear but detecting them as small targets in complex environments is challenging. This work proposes enhancements to the YOLOv8 object detection frame-work, specifically incorporating a spatial-to-depth (SPD) convolution module and a large selective kernel network (LSKNet). SPD-Conv combines spatial-to-depth layers and non-strided convolutions to retain fine-grained information when downscaling feature maps, while LSKNet introduces dynamic spatial selection and attention for refined context modeling. Our customized model is trained on a dataset of construction hardhat images captured via drones. Quantita-tive results showcase higher precision and recall over baseline YOLOv8, surpassing competing YOLOv5 versions. An optimized final model outcomes demonstrate accuracy exceeding 90% validation in mAP metric after 200 training rounds. By tackling limitations posed by small, obscured industrial safety gears, this enhanced real-time detection approach provides indispensable technological support for bolstering workplace hazard identification and prevention. © 2023, University of Zagreb Faculty of Electrical Engineering and Computing. All rights reserved.