Improved YOLOV8 Network and Application in Safety Helmet Detection

Abstract This paper proposes a research method to enhance the accuracy and real-time capability of helmet detection in complex industrial environments, aiming to address the engineering challenges of poor robustness and significant occurrences of both false positives and false negatives in existing detection methods. In this study, the C2F (faster version of CSP Bottleneck with two convolutions) module and FE (FasterNet with EMA) module are integrated into the network architecture of YOLOV8 to form a new attention mechanism module called C2F-FE. This module enhances the model’s perception of safety helmet targets by fusing feature information from different levels and incorporating attention mechanisms while reducing computational overhead. Furthermore, the model is trained and optimized on publicly available safety helmet datasets. Experimental results demonstrate that the improved model exhibits stronger robustness, achieving an accuracy rate of 94.6% and a mAP50 of 99.1% for safety helmet detection in complex construction scenarios, with an inference time of 0.7 ms.