To address the issues of large parameter and computation requirements, low detection accuracy, and the difficulty of efficiently deploying large models in safety helmet and reflective vest detection, an improved lightweight detection algorithm, CCEI-YOLOv8, is proposed. In this algorithm, the C2f-CIB module is adopted in the backbone and neck networks; the neck network is reconstructed with a cross-scale feature fusion module(CCFM); the EMA coordinate attention mechanism is introduced; and CIoU is replaced with Inner-EIoU to enhance regression localization accuracy. The effectiveness of the proposed algorithm is demonstrated through experiments conducted on the open-source Roboflow dataset for safety helmets and reflective vests. The results show that the algorithm achieves significant improvements: Parameters are reduced by 48.3%; computation is decreased by 32.1%; and the mean Average Precision(mAP@50) is increased by 0.5%, reaching 91.7%. The model size is reduced to only 3.442 MB, a decrease of 45%. Compared to the original YOLOv8n and other mainstream detection algorithms, CCEI-YOLOv8 demonstrates superior detection accuracy and lightweight design. This makes it highly suitable for real-time detection and deployment, providing a valuable reference for the real-time detection of safety helmets and reflective vests.