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Abstract: To address the challenges of large scale variations and strong background interference in dense crowd images, this paper proposes a crowd counting model named Multi-scale Selective Density Aggregation Network (MSDA-Net). The model introduces three collaborative modules: the Multi-scale Tuning Module (MTM), the Selective Attention Module (SAM), and the Density Context-aware Module (DCF), which collectively enhance feature representation and density map quality, thereby improving counting accuracy in complex congested scenes. Specifically, the MTM captures multi-scale contextual information through multi-branch atrous convolutions, alleviating the impact of drastic scale variations. The SAM employs spatial and channel attention mechanisms to suppress background noise and highlight foreground crowd regions. The DCF models the contextual dependencies of density maps using convolutional LSTM architecture, enhancing continuity between adjacent regions to generate smoother and more accurate density maps. A joint loss function combining Euclidean distance loss and structural similarity (SSIM) loss is adopted to optimize pixel-level regression and local structural consistency simultaneously. Extensive experiments are conducted on four public benchmarks, including ShanghaiTech Part_A, ShanghaiTech Part_B, UCF_CC_50, and UCF_QNRF. The proposed MSDA-Net achieves superior performance on all datasets, e.g., MAE of 58.1 on Part_A, 6.9 on Part_B, 205.3 on UCF_CC_50, and 84.8 on UCF_QNRF, outperforming many state-of-the-art methods. Ablation studies further validate the individual contribution of each module. Overall, MSDA-Net demonstrates strong generalization ability and practical application value in real-world crowded scenes.Abstract: To address the challenges of large scale variations and strong background interference in dense crowd images, this paper proposes a crowd counting model named Multi-scale Selective Density Aggregation Network (MSDA-Net). The model introduces three collaborative modules: the Multi-scale Tuning Module (MTM), the Selective Attention Module (SAM), and ...Learn More
Abstract: In response to the paradigm shift of the Internet of Things (IoT) towards sensing-communication-computing integration and AI-native design driven by 6G, this paper systematically establishes an evolution roadmap and innovation framework for emerging IoT technologies. The core contribution lies in breaking the limitation of "pure connectivity" in traditional IoT and reshaping the future network architecture from three dimensions: spectrum revolution, native intelligence, and multi-dimensional fusion. It uniquely distills six core technical characteristics for 6G-driven IoT, including space-air-ground integration and distributed networking. Methodologically, the work deeply analyzes the architectural innovation and closed-loop management of Passive IoT (P-IoT) and AIoT, prioritizes technical pain points such as "time-varying network representation" and "time-deterministic routing" in satellite and Non-Terrestrial Network (NTN) IoT, and forward-lookingly expands to the Internet of Space Things (IoST) for interplanetary exploration. By charting a coherent technical pathway, this review provides a high-value theoretical foundation and deployment blueprint for massive, ubiquitous, and green next-generation IoT systems.Abstract: In response to the paradigm shift of the Internet of Things (IoT) towards sensing-communication-computing integration and AI-native design driven by 6G, this paper systematically establishes an evolution roadmap and innovation framework for emerging IoT technologies. The core contribution lies in breaking the limitation of "pure connectivity" in tr...Learn More
