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Object detection plays a pivotal role in various fields, for example, a smart traffic system relies on the detected results for decision-making. However, existing studies predominately utilize optical camera and LiDAR, which exhibit limitations in adverse outdoor environments, such as foggy weather. To address these challenges, millimeter-waves (mmWaves) attract researchers’ attention to detect objects in severe conditions since they can work effectively in low-visibility conditions and overcome small obstacles. Yet, previous mmWave-based works have shown limited performance, such as no shape information for objects. Therefore, we design and implement a two-stage system,mmBox, to accurately predict bounding boxes with depth for vehicles and pedestrians, which first generates heatmaps in different dimensions and then leverages a deep learning model to extract features for predictions. To evaluate the performance ofmmBox, we collected real-world mmWave reflections from urban traffic intersections and dense-fog environments. The extensive evaluation metrics show remarkable accuracy and the low latency of our model.