Abstract: Aiming at the problem that rotating small objects are difficult to detect in subway scenes, this paper proposes a rotating small object detection method based on high-resolution networks and rotating target frames in subway scenes. The high-resolution network and FPN feature fusion head were used to enhance the feature extraction ability, and the strategy of rotating Gaussian kernel and rotating target detection frame was proposed to adapt to target detection in any direction. The experimental results show that these improvements make the model detection accuracy in the detection of rotating small targets and oblique slender targets much higher than that of applying horizontal detection target boxes. The method can accurately detect horizontal targets in the subway scene, but also can accurately detect rotating small targets and slender inclined targets.