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In recent years, due to the high number of unsafe factors in the construction industry, its death rate and injury rate have become the focus of research in the field of target detection. In the complex environment of the construction site, workers often forget to wear helmets or fail to wear them properly, causing hidden dangers for the safety of workers. Therefore, this paper proposes an application-oriented improvement strategy for helmet detection based on the YOLOv8 architecture (EC-YOLOv8), which integrates the ECA attention mechanism and content-sensing recombination feature operator into the YOLOv8 network, so as to further improve detection accuracy and detection time, and better meet the actual application requirements of construction sites. Enhanced Intersection over Union Loss (EIoU Loss) is introduced to improve the network model’s ability to evaluate the difference between the predicted boundary box and the real boundary box. The experimental results show that the EC-YOLOv8 algorithm proposed in this paper achieves 95.7% accuracy on the data set SHWD, which improves the detection accuracy of the helmet target.