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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. 

We presentCoSense, a system that enables coexistence of networking and sensing on next-generation millimeter-wave (mmWave) picocells for traffic monitoring and pedestrian safety at intersections in all weather conditions. Although existing wireless signal-based object detection systems are available, they suffer from limited resolution and their outputs may not provide sufficient discriminatory information in complex scenes, such as traffic intersections.CoSenseproposes using 5G picocells, which operate at mmWave frequency bands and provide higher data rates and higher sensing resolution than traditional wireless technology. However, it is difficult to run sensing applications and data transfer simultaneously on mmWave devices due to potential interference, and using special-purpose sensing hardware can prohibit deployment of sensing applications to a large number of existing and future inexpensive mmWave devices. Additionally, mmWave devices are vulnerable to weak reflectivity and specularity challenges, which may result in loss of information about objects and pedestrians. To overcome these challenges,CoSensedesign customized deep learning models that not only can recover missing information about the target scene but also enable coexistence of networking and sensing. We evaluateCoSenseon diverse data samples captured at traffic intersections and demonstrate that it can detect and locate pedestrians and vehicles, both qualitatively and quantitatively, without significantly affecting the networking throughput. 

D-band millimeter-wave, a key wireless technology for beyond 5G networks, promises extremely high data rate, ultra-low latency, and enables new Internet of Things applications. However, massive signal attenuation, complex response to building structures, and frequent non-availability of the Line-Of-Sight path make D-band picocell deployment challenging. To address this challenge, we propose a deep learning-based tool, that allows a network deployer to quickly scan the environment from a few random locations and predict Signal Reflection Profiles everywhere, which is essential to determine the optimal locations for picocell deployment.