A fundamental challenge in remote sensing-based forest fire detection lies in accurately discerning fire characteristics on various scales against the backdrop of intricate and heterogeneous forest landscapes. In response to this challenge, we propose a dual-path network (DPMNet) with multidimensional feature interaction for real time remote sensing forest fire detection. Initially, a dual-path backbone network is designed, integrating coarse-grained and fine-grained parallel pathways, working in tandem to capture both global visual features and nuanced local texture details. Subsequently, we develop the Multidimensional Interactive Feature Pyramid Network (MiFPN), a novel structure that amalgamates information streams from varied levels through a three-branch structure and engenders profound fusion and dynamic interaction of features across multiple scales. Thereafter, the Context-Enriched Adaptive Fusion Module (CEAFM) is proposed, which emerges to meticulously blend macroscopic visual elements harvested via coarse-grained conduits, employing a multi-faceted pathway strategy to bolster the model’s overarching comprehension and precision in forest fire detection. Finally, the Enhanced Contextual Pooling Bottleneck (ECPB) is put forward, an integration that augments the model’s spatial perception and contextual acumen through the incorporation of dilated convolution and global pooling techniques. Extensive experiments are conducted on the remote sensing forest fire dataset in order to confirm the efficacy of DPMNet. The experimental results demonstrate that our DPMNet achieves satisfactory performance in terms of real-time performance as well as accuracy and provides an effective solution for real-time detection of remote sensing forest fires based on UAVs.

Real-time detection of forest fires through remote sensing is a challenging task, especially in the context of limited data availability. In response to this challenge, this article leverages the digital twin (DT) concept to create a comprehensive and high-fidelity synthetic forest wildfire dataset. Alongside this, we have made available a high-resolution forest fire remote sensing dataset from real scenarios, meticulously collected and annotated by our research team. Aiming for precision in detecting forest fires via remote sensing, we present the remote sensing forest wildfire detection network (RFWNet) and its lightweight version, RFWNet-nano. More specifically, our network’s backbone, grounded on deformable convolution network v3 (DCNv3), develops a multigroup mechanism, amplifying its ability to perceive the correlations overextended distances. Utilizing our dual-path dynamic sparse attention (DDSA), we meld coarse-grained regional selection with granular token-to-token attention, adeptly capturing the evolving contours of fires and smoke. To address diverse scenarios, our Vanilla Head design, backed by a profound training approach and simultaneous stacked activations, accurately identifies flames and smoke across multiple scales. Furthermore, we advocate for a 24/7 real-time monitoring system, synergizing drones, edge computing devices, and NVIDIA GPUs. Our experimental outcomes indicate that relative to numerous prevailing object detection algorithms, RFWNet and RFWNet-nano both manifest considerable superiority in terms of quantitative precision and visual results, substantiating the robustness and preeminence of our methodologies.

How to ensure the reliability of mixed communication between vehicle to vehicle and vehicle to road, and how to ensure seamless handover during communication in the Internet of Vehicles, is a research hotspot in 5G cellular Internet of Vehicles. This paper proposes a 5G cellular Internet of Vehicles communication handover strategy model for V2V (Vehicle to Vehicle) and V2I (Vehicle to Infrastructure) scenarios, which combines fuzzy logic and user satisfaction for obtaining quality of service, and can make intelligent decisions on handover trigger timing and handover targets based on various parameters in the wireless network, so that the vehicle can handover to the best service target in the process of vehicle terminal handover. The experimental study shows that the proposed method outperforms other common methods in terms of handover metrics such as handover trigger rate, handover times and quality of service compared to existing studies.

In the cloud-edge collaborative computing environment, how to allocate appropriate edge computing and cloud computing resources for tasks according to their quality of service requirements is the key to ensuring the effectiveness of cloud-edge collaboration. Aiming at the decision-making problem of offloading tasks with different deadlines in the cloud-edge collaborative system, a strategy of prioritizing tasks based on deadlines and then allocating cloud, edge computing resources, and communication resources based on priority is proposed. The theoretical analysis model and heuristic algorithm are presented, and then the performance of the proposed theoretical model and the algorithm is verified and analyzed through simulation experiments.

Abstract Object detection is one of the main tasks of computer vision. Object detection algorithms usually rely on deep convolutional neural networks, which require the host device to have high computing capabilities, greatly limiting the application of object detection methods for mobile devices with limited computing capabilities, such as embedded devices. Among the current object detection algorithms, the you only look once (YOLO) series takes both speed and accuracy into consideration and is one of the most commonly used methods for object detection. In this article, TRC‐YOLO is proposed, which improves the mean average precision (mAP) and real‐time detection speed of the model while reducing the size of the model. In TRC‐YOLO, the convolution kernel of YOLO v4‐tiny is pruned and an expansive convolution layer is introduced into the residual module of the network to produce an hourglass Cross Stage Partial ResNet (CSPResNet) structure. A receptive field block (RFB) that simulates human vision is also added, increasing the receptive field of the model and strengthening the feature extraction ability of the network. In addition, the convolutional block attention module is applied, which combines spatial attention and channel attention, to enhance the effective features of the model and reduce the negative impact of noise on the model. The size of the TRC‐YOLO model is 17.8 MB, which is 5.9 MB smaller than YOLO v4‐tiny, and the model parameter is 2.983 billion floating point operations per second (BFLOP/s) (3.834 BFLOP/s less than YOLO v4‐tiny). In addition, TRC‐YOLO achieves a real‐time performance of 36.9 frames per second on a Jetson Xavier NX, and its mAP on the PASCAL VOC dataset is 66.4 (3.83 higher than YOLO v4‐tiny). In addition, the mAP of TRC‐YOLO on the MS COCO dataset is 37.7, which is 1.9 higher than that of the baseline model.

Abstract This paper introduce an efficient object detection network named Trident‐You Only Look Once (YOLO), which is designed for mobile devices with limited computing power. The new architecture is improved based on YOLO v4‐tiny. The authors redesign the network structure and propose a trident feature pyramid network (Trident‐FPN), which can improve the precision and recall of lightweight object detection. Specifically, Trident‐FPN increases the computational complexity by only a small amount of floating point operations per second (FLOPs) and obtains a multi‐scale feature map of the model, which significantly lightweight object detection performance. To enlarge the receptive field of the network with the fewest FLOPs, this paper redesign the receptive field block (RFB) and spatial pyramid pooling (SPP) layer and propose tinier cross‐stage partial RFBs and smaller cross‐stage partial SPPs. This paper present extensive experiments, and Trident‐YOLO shows strong performance compared to that of other popular models on the PASCAL VOC and MS COCO. On the MS COCO and PASCAL VOC 2007 test sets, the mean average precision (mAP) of Trident‐YOLO improved by 4.5% and 5.0%, respectively. Trident‐YOLO also reduce the network size by more than 54.4% compared to YOLO v4‐tiny. With a 23.7% FLOP reduction, the FPS is improved by 1.9 on an Nvidia Jetson Xavier NX.

A single infrared image or visible image for the same scene is usually insufficient to simultaneously reveal the infrared objects and the scene details. Thus, image fusion techniques play an important role in producing a single image from the images captured by infrared and visible sensors. In this paper, we propose a novel total variation (TV)-based fusion for infrared and visible images. In our model, a weighted fidelity term is employed to fuse both the infrared objects in the infrared image and the salient scenes in the visible image. To this end, a weight estimation method is developed based on the global luminance contrast-based saliency. Also, to overcome the over-fitting, two constraints are further introduced to merge more details from the visible image and prevent the luminance degradation for the fused result, respectively. Moreover, joint norms are exploited to produce a better result.${{\boldsymbol{l}}_{2,1,{\boldsymbol{rc}}}}$provides the structural group sparseness for the fidelity term, whereas${{\boldsymbol{l}}_{1/2}}$presents the better gradient sparse for the detail preserving term and${{\boldsymbol{l}}_2}$is utilized for the luminance degradation preventing term. Experimental results indicate that the proposed method can give state-of-the-art performances both in visual perception and quantitative scores than other methods.

Most of the existing work about device-to-device (D2D) data offtoading do not simultaneously consider the mobile scenario and the trade-off between the revenue and cost of caching data. In this paper, we consider a more comprehensive and practical scenario of D2D data offloading, and design a mobility-aware incentive mechanism to efficiently select some mobile users to pre-cache the proper contents with the objective of maximizing social welfare by jointly considering mobile users' preference similarity and the social relationship. Simulation results demonstrates that the proposed mechanism outperforms other counterparts. In addition, the proposed mechanism satisfies the nice properties of individual rationality and truthfulness.

Contention‐based carrier sense multiple access (CSMA) and contention‐free time division multiple access (TDMA) protocol are two typical access protocols of media access control in vehicular ad hoc network (VANET). They all show their unique advantages under specific conditions. TDMA has high transmission reliability, CSMA has a low transmission delay in the communication environment with low packet arrival rate. However, when the arrival rate of information packets increases rapidly, the throughput of CSMA will decrease rapidly and approach zero, which is not suitable for data transmission in the communication environment with a high arrival rate of information packets; while data transmission through a single TDMA protocol will cause high system overhead due to strict synchronous information. Aiming at the defects of the two protocols and the multi‐channel communication environment, this study proposes the optimised protocol model multi‐priority time division‐CSMA (MPTD‐CSMA). The optimised protocol model not only ensures the reliability of data communication but also reduces the transmission delay of the system. At the same time, the multi‐priority mechanism is added to increase the channel utilisation of the protocol model.