Object detection with camera has achieved promising results using deep learning methods, but it suffers degraded performance under adverse conditions (e.g., foggy weather, poor illumination). To remedy this, some recent studies resort to leveraging the complementary mmWave radar, which is less affected by adverse conditions, and designing effective fusion methods. However, the existing early fusion methods are vulnerable to data noise, while the existing late fusion methods ignore the association of object information between feature maps in the early stage. To overcome these shortcomings, we propose a Global-Local Feature Enhancement Network (GLE-Net), a two-stage deep fusion detector, which first generates anchors from two sensors and uses an auxiliary module to locally enhance the single-branch missing proposals, and then fuses the global features from the multimodal sensors to improve final detection results. We collect two datasets under foggy weather and poor illumination conditions with diverse scenes, and conduct extensive experiments, verifying that the proposed GLE-Net surpasses other state-of-the-art methods in terms of Average Precision (AP).

Energy and cost are two primary concerns when leveraging drones for urban sensing. With the advances of wireless charging technologies and the inspiration from the sparse crowdsensing paradigm, this paper proposes a novel drone-based collaborative sparse-sensing framework DroneSense, demonstrating its feasibility for sustainable urban-scale sensing. We focus on a typical use case, i.e., leveraging DroneSense to sense open parking spaces. DroneSense selects a minimum number of Points of Interest (POIs) to schedule drones for physical data sensing and then infers the parking occupancy of the remaining POIs to meet the overall quality requirement. However, drone-based sensing is different from human-centric crowdsensing, resulting in a series of new problems, including which POIs are visited first, when and where to charge drones, which drones to charge first, how much to charge, and when to stop the scheduling. To this end, we design a holistic solution, including context-aware matrix factorization for parking occupancy data inference, progressive determination of task quantity, deep reinforcement learning (DRL) based task selection, energy-aware DRL-based task scheduling, and adaptive charger scheduling. Extensive experiments with a real-world on-street parking dataset from Shenzhen, China demonstrate the obvious advantages of DroneSense.