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.

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.

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 paper leverages the digital twin 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 scenario, 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 multi-group mechanism, amplifying its ability to perceive the correlations over extended 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.

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.

Mie surface lattice resonances (SLRs) supported by periodic all-dielectric nanoparticles emerge from the radiative coupling of localized Mie resonances in individual nanoparticles through Rayleigh anomaly diffraction. To date, it remains challenging to achieve narrow bandwidth and active tuning simultaneously. In this work, we report extremely narrow and actively tunable electric dipole SLRs (ED-SLRs) in Ge₂Se₂Te₅ (GST) metasurfaces. Simulation results show that, under oblique incidence with TE polarization, ED-SLRs with extremely narrow linewidth down to 12 nm and high quality factor up to 409 can be excited in the mid-infrared regime. By varying the incidence angle, the ED-SLR can be tuned over an extremely large spectral region covering almost the entire mid-infrared regime. We further numerically show that, by changing the GST crystalline fraction, the ED-SLR can be actively tuned, leading to nonvolatile, reconfigurable, and narrowband filtering, all-optical multilevel modulation, or all-optical switching with high performance. We expect that this work will advance the engineering of Mie SLRs and will find intriguing applications in optical telecommunication, networks, and microsystems.

The Kerker effect has been generalized in nanophotonics and meta-optics, and has recently been of great interest by relating it to various fascinating functionalities such as scattering management and perfect transmission, reflection or absorption. One of the most interesting generalizations is the resonant lattice Kerker effect in periodic nanostructures. However, its active tuning has not been explored yet. Here, we report, for the first time, the active control of the resonant lattice Kerker effect in periodic Ge₂Se₂Te₅ nanodisks. By changing the crystalline fraction, we show that the electric dipole surface lattice resonance (ED-SLR), the magnetic dipole resonance (MDR), and thus the resonant lattice Kerker effect are all red-shifted. We therefore realize the transition from the ED-SLR to the resonant lattice Kerker effect, which enables multilevel tuning of reflection, transmission and absorption with modulation depths above 86%. Taking advantage of the MDR redshifts, we also observe broadband and multilevel tuning of transmissions with modulation depth of 87% over a broadband range of 588 nm. Our work establishes a new path for designing high-performance active nanophotonic devices.