A helmet detection algorithm based on improved Yolov8n

Abstract To address the issues of false positives and missed detections in current safety helmet algorithms, we propose a new detection method using an enhanced yolov8n model. An enhanced Yolov8n-based algorithm for detecting safety helmets is proposed. In this experiment, a new module called C2f_ECA is proposed. This module can extract channel information and enhance the performance of the network by replacing the original loss function of the algorithm with the EIoU loss function. The aspect ratio loss term is now determined by the difference between the predicted dimensions of width and height and those of the minimum bounding box. This modification speeds up the convergence of predicted boxes and improves their regression accuracy. In addition, a dataset of safety helmets is constructed in complex scenarios. The proposed algorithm enhances Yolov8n’s mAP50 by 1.6% and average accuracy by 1.6% compared to the original and traditional Yolov8 algorithms, respectively. It can efficiently address the issues of false positives and missed detections of safety helmets.