In this paper, we focus on evaluating the robustness of helmet detection in the context of traffic surveillance, achieved through state-of-the-art deep learning models. This aims to contribute significantly to motorcycle safety by implementing intelligent systems adept at accurately identifying helmets. An integral component of this inquiry entails a meticulous benchmark of cutting-edge object detection models and the integration of advanced techniques, aiming not only to bolster accuracy but also to improve the overall practicality and effectiveness of helmet detection systems. The experimental results highlight the effectiveness of the state-of-the-art object detection methods in detecting helmets and the potential of transferring from the traffic domain to the construction site domain.