Region-based detectors with convolutional neural networks tend to learn textural rather structural feature and thus face substantial difficulties in detecting objects with various textures. To tackle this problem, the texture randomization to augment the synthetic training image dataset was employed and a novel method for structure-aware object detection was proposed. The texture-randomized simulation data were generated by rendering 3D model with varied textures using Blender. Experiments on synthetic and real images indicate that the proposed method is capable of robustly detecting texture-varied objects based on structural information.