Multi-drone multi-target association suffers from cognitive conflicts in multi-perspective scenarios due to chain rule aggregation. This letter defines the Conflict-Free Association (CFA) task and proposes Connected Subgraph-Based Heuristic Association (CSHA) with a plug-and-play Conflict Resolution Module (CRM). By mapping associations to graphs and searching for conflict-free subgraphs via a greedy algorithm, CSHA resolves conflicts effectively. Experiments on Rflysim and real-world data validate its superiority.

Neural networks have been extensively applied across various Internet of Things (IoT) applications, such as drone- and satellite-based remote sensing and autonomous driving. With the increasing resolution and amount of data captured by sensors, the demand for real-time response in IoT applications is markedly increasing. However, it is difficult for existing convolutional neural network (CNN) accelerators for IoT applications on field-programmable gate array (FPGA) platforms to achieve high throughput because of the inherent dense multiplication operations of CNNs, memory bandwidth limitations and inefficient mapping mechanisms. In this article, a high-throughput and energy-efficient all adder neural network (A2NN) accelerator for IoT applications on FPGA platform is proposed to solve this problem. First, a series of hardware-oriented algorithm optimization methods are proposed to simplify the processing flow of A2NN and further minimize its deployment overhead. Second, a novel hardware architecture based on the idea of near-memory computation (NMC) is proposed to eliminate off-chip memory access completely and accelerate the reconstructed A2NN in the pipeline. Third, a set of quantitative analysis methods for the proposed accelerator is presented to balance throughput and energy consumption, allowing the accelerator to adapt to the varying demands of different IoT application scenarios. Extensive experimental results on the AMD-Xilinx VC709 board demonstrate that the proposed accelerator achieves state-of-the-art performance in terms of throughput, energy efficiency, and throughput efficiency. Moreover, experiments on the AMD-Xilinx KV260 board highlight the architecture’s exceptional scalability and energy efficiency, enabling a balance between speed and power consumption tailored to the specific requirements of IoT application scenarios.

Convolutional Neural Networks (CNNs) have achieved remarkable breakthroughs on remote sensing tasks in recent years. However, deploying CNNs for real-time remote sensing on-board processing still remains a challenge due to power consumption, real-time and other limitations. Therefore, in this article, a satellite-based real-time remote sensing accelerator is proposed, where algorithm and hardware approaches are proposed to jointly optimize CNNs’ deployment on edge-side aerospace devices. Firstly, a collaborative-trainable sparse-quantization (CTSQ) method is proposed to reduce the model’s storage overhead. In the CTSQ method, analysis of the errors is performed for the sparsity-quantization composition. Besides, the inter-channel correlations among parameters are leveraged, where the structured sparsity and quantization are performed with fine-grained units. Secondly, a modular-system co-optimized (MoSyC) architecture is proposed. A hardware-mapped sparse access (HMSA) strategy is proposed to effectively filter out zero elements in sparse parameters. Moreover, a high-throughput architecture is designed for parallel and pipelined data flow control. Finally, extensive experiments are conducted on both scene classification and object detection tasks with ResNet and YOLOv5 models. The results show that the proposed CTSQ method achieves the compression ratio of more than 13.81 times, and the proposed MoSyC architecture achieves the throughput of more than 1815 GOPS, demonstrating the effectiveness of the proposed accelerator.

Scene classification has become an active research area in remote sensing (RS) image interpretation. Recently, Transformer-based methods have shown great potential in modeling global semantic information and have been exploited in RS scene classification. In this letter, we propose a multi-level fusion Swin Transformer (MFST), which integrates a multi-level feature merging (MFM) module and an adaptive feature compression (AFC) module to further boost the performance for RS scene classification. The MFM module narrows the semantic gaps in multi-level features via patch merging in lower-level feature maps and lateral connections in the top-down pathway. The AFC module makes multi-level features have smaller dimensions and more coherent semantic information by adaptive channel reduction. We evaluate the proposed network on the aerial image dataset (AID) and NWPU-RESISC45 (NWPU) datasets, and the classification results reveal that the proposed network outperforms several state-of-the-art (SOTA) methods.

Recently, convolutional neural network (CNN)-based remote sensing scene classification has achieved great success. However, the prohibitively expensive computation and storage requirements of state-of-the-art models have hindered the deployment of CNNs on on- board platforms. In this letter, we propose a differentiable neural architecture search (NAS)-based channel pruning method to automatically prune the CNN models. In the proposed method, the importance of each output channel is measured by a trainable score. The scores are optimized by an NAS method to search a good-performance pruned structure. After the search process, a global score threshold is adopted to derive the pruned model. A cost-awareness loss is proposed for the search process to encourage the floating-point operation (FLOP) compression ratio of the pruned model coverage to a desired value. We apply the proposed method to ResNet-34 and VGG-16 to verify the performance. The NWPU-RESISC-45 and UC Merced Land-Use (UCM) datasets are used for the performance evaluation. A comparison with state-of-the-art pruning methods demonstrates that the proposed method can achieve competitive performance with a similar reduction in FLOP.

Currently, remote-sensing (RS) scene classification has played an important role in many practical applications. However, traditional methods that are based on deep convolutional neural networks (DCNNs) have several difficulties when faced with data shift problems: novel classes, varied orientations, and large intraclass variations of RS scene images. In this letter, we propose the rotation-invariant and discriminative-learning prototypical networks (RDPNs) for RS scene classification. RDPN uses A-ORConv32 basic blocks and attention mechanisms to obtain rotation-invariant and discriminative features. In addition, adaptive cosine center loss is proposed to constrain the features to mitigate the large intraclass variations and penalize the hard samples adaptively. We conduct extensive experiments on publicly available datasets and achieve 1.69%–19.38% higher accuracy than existing methods. The experimental results verify that the proposed RDPN can solve the data shift problems well in RS scene classification.