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EMG-YOLO: An efficient fire detection model for embedded devices EMG-YOLO:嵌入式设备的高效火灾探测模型
IF 2.9 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-10-18 DOI: 10.1016/j.dsp.2024.104824
Linsong Xiao , Wenzao Li , Xiaoqiang Zhang , Hong Jiang , Bing Wan , Dehao Ren
The number of edge embedded devices has been increasing with the development of Internet of Things (IoT) technology. In urban fire detection, improving the accuracy of fire detection based on embedded devices requires substantial computational resources, which exacerbates the conflict between the high precision needed for fire detection and the low computational capabilities of many embedded devices. To address this issue, this paper introduces a fire detection algorithm named EMG-YOLO. The goal is to improve the accuracy and efficiency of fire detection on embedded devices with limited computational resources. Initially, a Multi-scale Attention Module (MAM) is proposed, which effectively integrates multi-scale information to enhance feature representation. Subsequently, a novel Efficient Multi-scale Convolution Module (EMCM) is incorporated into the C2f structure to enhance the extraction of flame and smoke features, thereby providing additional feature information without increasing computational complexity. Moreover, a Global Feature Pyramid Network (GFPN) is integrated into the model neck to further enhance computational efficiency and mitigate information loss. Finally, the model undergoes pruning via a slimming algorithm to meet the deployment constraints of mobile embedded devices. Experimental results on customized flame and smoke datasets demonstrate that EMG-YOLO increases mAP@50 by 3.2%, decreases the number of parameters by 53.5%, and lowers GFLOPs to 49.8% of those in YOLOv8-n. These results show that EMG-YOLO significantly reduces the computational requirements while improving the accuracy of fire detection, and has a wide range of practical applications, especially for resource-constrained embedded devices.
随着物联网技术的发展,边缘嵌入式设备的数量不断增加。在城市火灾探测中,提高基于嵌入式设备的火灾探测精度需要大量的计算资源,这加剧了火灾探测所需的高精度与许多嵌入式设备的低计算能力之间的矛盾。为解决这一问题,本文介绍了一种名为 EMG-YOLO 的火灾探测算法。其目标是在计算资源有限的嵌入式设备上提高火灾探测的精度和效率。首先,本文提出了一个多尺度注意力模块(MAM),它能有效整合多尺度信息以增强特征表示。随后,在 C2f 结构中加入了新颖的高效多尺度卷积模块(EMCM),以增强火焰和烟雾特征的提取,从而在不增加计算复杂度的情况下提供额外的特征信息。此外,模型颈部还集成了全局特征金字塔网络(GFPN),以进一步提高计算效率并减少信息损失。最后,模型通过瘦身算法进行剪枝,以满足移动嵌入式设备的部署限制。在定制火焰和烟雾数据集上的实验结果表明,EMG-YOLO 将 mAP@50 提高了 3.2%,参数数量减少了 53.5%,GFLOPs 降低到 YOLOv8-n 的 49.8%。这些结果表明,EMG-YOLO 显著降低了计算要求,同时提高了火灾探测的准确性,具有广泛的实际应用前景,尤其适用于资源受限的嵌入式设备。
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引用次数: 0
An efficient infinity norm minimization algorithm for under-determined inverse problems 欠确定逆问题的高效无穷规范最小化算法
IF 2.9 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-10-17 DOI: 10.1016/j.dsp.2024.104818
Ahmad M. Rateb
The problem of solving under-determined systems of linear equations with minimum peak magnitude ( norm) has numerous applications in signal processing. These include Peak-to-Average Power Ratio (PAPR) reduction in MIMO-OFDM systems, vector quantization, approximate nearest neighbor search, optimal control in robotics, and power grid optimization. Several methods have been proposed to address this problem, but they often face limitations in computational speed or representation accuracy. Some methods also impose constraints on the frame matrix, such as restrictions on the type of its entries or its aspect ratio. In this paper, we present the Fast Iterative Peak Shrinkage Algorithm (FIPSA), which iterates over feasible solutions to consistently reduce peak magnitude and provably converge to near-optimal solutions. Our experimental results, conducted across various frame matrix types and aspect ratios, demonstrate that FIPSA consistently achieves near-minimal norm values. In addition, it operates 1.3 to 7.3 times faster than previous methods, while maintaining an average representation error of 1015. Notably, these advancements are achieved without imposing any constraints on the frame matrix.
以最小峰值(ℓ∞ norm)求解未定线性方程组的问题在信号处理中有着广泛的应用。这些应用包括降低 MIMO-OFDM 系统中的峰均功率比 (PAPR)、矢量量化、近似近邻搜索、机器人技术中的最优控制以及电网优化。目前已提出了几种方法来解决这一问题,但这些方法往往在计算速度或表示精度方面受到限制。有些方法还对帧矩阵施加了限制,如对条目类型或长宽比的限制。在本文中,我们提出了快速迭代峰值收缩算法(FIPSA),该算法对可行的解决方案进行迭代,以持续降低峰值幅度,并可证明收敛到接近最优的解决方案。我们在各种帧矩阵类型和宽高比上进行的实验结果表明,FIPSA 能够持续实现接近最小的 ℓ∞ 规范值。此外,它的运行速度比以前的方法快 1.3 到 7.3 倍,而平均表示误差保持在 10-15 之间。值得注意的是,这些进步是在不对帧矩阵施加任何约束的情况下实现的。
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引用次数: 0
Joint range and velocity super-resolution estimation with Doppler effects for innovative OFDM-based RFA RadCom system 利用多普勒效应对基于 OFDM 的创新型 RFA RadCom 系统进行联合测距和速度超分辨率估算
IF 2.9 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-10-15 DOI: 10.1016/j.dsp.2024.104805
Wenxu Zhang , Hao Wan , Zhongkai Zhao , Manjun Lu
Conventional radar and communication signals face challenges when integrating for accurate sensing in dense electromagnetic environments, especially in scenarios involving high-velocity targets estimation. To address this issue, we propose the random frequency agile-orthogonal frequency division multiplexing-based radar and communication (RFA-OFDM-based RadCom) signal, a novel framework that combines RFA hopping radar signal and OFDM signal. This framework effectively handles high-velocity Doppler scenarios, enhancing electronic countermeasure capabilities. In high-velocity scenarios, achieving accurate range and velocity estimation is crucial. We introduce a comprehensive received signal model that considers intrapulse and intersubcarrier Doppler effects, often overlooked in traditional high-velocity contexts. The proposed two-phase hierarchical perceptual methodology enables joint super-resolution estimation using the shared signal. We transform the shared signal echo model into a uniform linear array-like model and employ the matrix decomposition algorithm based on bidirectional weighted frequency smoothing (BWFS-MD) for decoherence processing. Subsequently, the estimation of signal parameters via rotational invariance techniques (ESPRIT)-complementary integrated subspace fitting (E-CISF) algorithm accurately estimates joint range and velocity. Meanwhile, the contrastive analysis of the mutual impacts between radar and communication functions is conducted. Theoretical analysis and simulation results robustly validate the superior performance of the proposed BWFS-MD algorithm. Furthermore, considering the precision of joint range-velocity estimation, real-time constraints, and super-resolution capability (which is emphasized), the E-CSIF algorithm demonstrates the best overall performance from a comprehensive perspective.
传统的雷达和通信信号在密集电磁环境中进行整合以实现精确感知时面临挑战,尤其是在涉及高速目标估计的场景中。为解决这一问题,我们提出了基于随机频率敏捷-正交频分复用技术的雷达和通信(RFA-OFDM-based RadCom)信号,这是一种将随机频率敏捷跳变雷达信号和正交频分复用技术信号相结合的新型框架。该框架可有效处理高速多普勒场景,增强电子对抗能力。在高速场景中,实现精确的测距和速度估计至关重要。我们引入了一个全面的接收信号模型,该模型考虑了脉冲内和子载波间的多普勒效应,而这些效应在传统的高速情况下往往被忽视。所提出的两阶段分层感知方法能够利用共享信号进行联合超分辨率估计。我们将共享信号回波模型转换为均匀线性阵列模型,并采用基于双向加权频率平滑(BWFS-MD)的矩阵分解算法进行去相干处理。随后,通过旋转不变性技术(ESPRIT)-互补集成子空间拟合(E-CISF)算法估算信号参数,准确估算出关节范围和速度。同时,对雷达和通信功能之间的相互影响进行了对比分析。理论分析和仿真结果有力地验证了所提出的 BWFS-MD 算法的优越性能。此外,考虑到联合测距-测速估计的精度、实时约束和超分辨率能力(重点强调),E-CSIF 算法从综合角度展示了最佳的整体性能。
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引用次数: 0
A multi-rate sensor fusion and multi-task learning network for concurrent fault diagnosis of hydraulic systems 用于液压系统并发故障诊断的多速率传感器融合和多任务学习网络
IF 2.9 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-10-11 DOI: 10.1016/j.dsp.2024.104796
Shaohua Chen , Xiujuan Zheng , Huaiyu Wu
Hydraulic systems are widely used in key modern industrial fields such as mechanical manufacturing, aerospace, and heavy machinery, and their efficient and reliable operation is crucial to ensuring production safety and efficiency. However, hydraulic systems often experience concurrent faults, such as pump failures, valve blockages, pipeline leaks, and fluid contamination, which pose significant challenges to the fault diagnosis in hydraulic systems. This paper introduces a multi-task learning network that deconstructs the challenge of concurrent fault diagnosis into specific sub-tasks, enabling the simultaneous identification and classification of multiple hydraulic components' faults. Automatic channel filtering is designed to screen out sensitive channels of each component from multi-rate sensors. A dual-flow model is used to feature extraction, which can simultaneously extract the local spatial features and global semantic information. Then, four classification models are designed to identify the extracted shared features. An uncertainty weight loss is also proposed to balance the loss of different tasks. The experimental results show that our model significantly outperforms traditional methods and other popular multi-output methods in diagnosing concurrent faults.
液压系统广泛应用于机械制造、航空航天和重型机械等现代关键工业领域,其高效可靠的运行对确保生产安全和效率至关重要。然而,液压系统经常会出现并发故障,如泵故障、阀门堵塞、管路泄漏和流体污染等,这给液压系统的故障诊断带来了巨大挑战。本文介绍了一种多任务学习网络,可将并发故障诊断的挑战分解为特定的子任务,从而实现对多个液压元件故障的同时识别和分类。设计了自动通道过滤,以从多速率传感器中筛选出每个元件的敏感通道。采用双流模型进行特征提取,可同时提取局部空间特征和全局语义信息。然后,设计了四个分类模型来识别提取的共享特征。此外,还提出了一种不确定性权重损失,以平衡不同任务的损失。实验结果表明,在诊断并发故障方面,我们的模型明显优于传统方法和其他流行的多输出方法。
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引用次数: 0
A multi-scale dual-decoder autoencoder model for domain-shift machine sound anomaly detection 用于域转移机器声音异常检测的多尺度双解码器自动编码器模型
IF 2.9 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-10-11 DOI: 10.1016/j.dsp.2024.104813
Shengbing Chen, Yong Sun, Junjie Wang, Mengyuan Wan, Mengyuan Liu, Xiaofan Li
Anomaly detection through machine sounds plays a crucial role in the development of industrial automation due to its excellent flexibility and real-time response capabilities. However, in real-world scenarios, the occurrence frequency of machine anomaly events is relatively low, making it difficult to collect anomaly sound data under various operating conditions. Moreover, due to the influence of operating conditions and environmental noise, the collected sound data may have distribution differences, leading to data domain shifts issues. To address these problems, we propose an unsupervised multi-scale dual-decoder autoencoder (MS-D2AE) network for anomaly sound detection. The MS-D2AE model consists of residual layers, an encoder, and two decoders. The model fuses fine-grained information of sound features through the Multi-scale Feature Fusion Module (MTSFFM), enabling the model to effectively learn feature data from multiple scales. By using a residual layer composed of a single MTSFFM, the encoder's input is directly connected to the intermediate results, further enhancing information transmission. The designed dual-decoder autoencoder structure, in addition to reconstructing error calculation, also utilizes the similarity error calculation between the outputs of the two decoders, encouraging the model to more accurately reconstruct the feature data during learning, thus more comprehensively learning the feature representation of normal data. Additionally, to mitigate the impact of data shift on model performance, we design a feature domain mixing method that blends sound features from both source and target domains to enhance the diversity and generalization of sound features. Finally, we have verified the effectiveness of this method on the Dcase2023 Challenge Task2 and Dcase2022 Challenge Task2 datasets.
通过机器声音进行异常检测具有出色的灵活性和实时响应能力,因此在工业自动化发展中发挥着至关重要的作用。然而,在实际应用场景中,机器异常事件的发生频率相对较低,因此很难收集到各种运行条件下的异常声音数据。此外,由于工作条件和环境噪声的影响,采集到的声音数据可能存在分布差异,从而导致数据域转移问题。针对这些问题,我们提出了一种用于异常声音检测的无监督多尺度双解码器自动编码器(MS-D2AE)网络。MS-D2AE 模型由残差层、一个编码器和两个解码器组成。该模型通过多尺度特征融合模块(MTSFFM)融合声音特征的细粒度信息,使模型能够有效地学习来自多个尺度的特征数据。通过使用由单个 MTSFFM 组成的残差层,编码器的输入与中间结果直接相连,进一步加强了信息传输。所设计的双解码器自动编码器结构,除了重构误差计算外,还利用两个解码器输出之间的相似性误差计算,促使模型在学习过程中更准确地重构特征数据,从而更全面地学习正常数据的特征表示。此外,为了减轻数据偏移对模型性能的影响,我们设计了一种特征域混合方法,将源域和目标域的声音特征混合在一起,以增强声音特征的多样性和泛化能力。最后,我们在 Dcase2023 Challenge Task2 和 Dcase2022 Challenge Task2 数据集上验证了该方法的有效性。
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引用次数: 0
TAG-fusion: Two-stage attention guided multi-modal fusion network for semantic segmentation TAG-fusion:用于语义分割的两阶段注意力引导多模态融合网络
IF 2.9 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-10-11 DOI: 10.1016/j.dsp.2024.104807
Zhizhou Zhang, Wenwu Wang, Lei Zhu, Zhibin Tang
In the current research, leveraging auxiliary modalities, such as depth information or point cloud information, to improve RGB semantic segmentation has shown significant potential. However, existing methods mainly use convolutional modules for aggregating features from auxiliary modalities, thereby lacking sufficient exploitation of long-range dependencies. Moreover, fusion strategies are typically limited to singular approaches. In this paper, we propose a transformer-based multimodal fusion framework to better utilize auxiliary modalities for enhancing semantic segmentation results. Specifically, we employ a dual-stream architecture for extracting features from RGB and auxiliary modalities, respectively. We incorporate both early fusion and deep feature fusion techniques. At each layer, we introduce mixed attention mechanisms to leverage features from other modalities, guiding and enhancing the current modality's features before propagating them to the subsequent stage of feature extraction. After the extraction of features from different modalities, we employ an enhanced cross-attention mechanism for feature interaction, followed by channel fusion to obtain the final semantic features. Subsequently, we provide separate supervision to the network on the RGB stream, auxiliary stream, and fusion stream to facilitate the learning of representations for different modalities. The experimental results demonstrate that our framework exhibits superior performance across diverse modalities. Specifically, our approach achieves state-of-the-art results on the NYU Depth V2, SUN-RGBD, DELIVER and MFNet datasets.
在目前的研究中,利用深度信息或点云信息等辅助模态来改进 RGB 语义分割已显示出巨大的潜力。然而,现有方法主要使用卷积模块来聚合辅助模态的特征,因此缺乏对长距离依赖关系的充分挖掘。此外,融合策略通常仅限于单一方法。在本文中,我们提出了一种基于变换器的多模态融合框架,以更好地利用辅助模态来增强语义分割结果。具体来说,我们采用双流架构,分别从 RGB 和辅助模态中提取特征。我们采用了早期融合和深度特征融合技术。在每一层,我们都引入了混合注意力机制,以利用其他模态的特征,在将当前模态的特征传播到后续特征提取阶段之前,引导和增强当前模态的特征。从不同模态提取特征后,我们采用增强型交叉注意机制进行特征交互,然后进行通道融合,以获得最终的语义特征。随后,我们分别对网络的 RGB 流、辅助流和融合流进行监督,以促进不同模态的表征学习。实验结果表明,我们的框架在不同模态下均表现出卓越的性能。具体来说,我们的方法在纽约大学深度 V2、SUN-RGBD、DELIVER 和 MFNet 数据集上取得了最先进的结果。
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引用次数: 0
Large-scale multi-view spectral clustering based on two-stage well-distributed anchor selection 基于两阶段良好分布锚点选择的大规模多视角光谱聚类
IF 2.9 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-10-11 DOI: 10.1016/j.dsp.2024.104815
Xinran Cheng, Ziyue Tang, Xinmu Qi, Xinyi Qiang, Huamei Xi, Xia Ji
Spectral clustering has attracted much attention because of its good clustering effect, but its high computational cost makes it difficult to apply to large-scale multi-view clustering. In response to this issue, a simple and efficient large-scale multi-view spectral clustering algorithm is proposed, which is based on a Two-stage Well-distributed Anchor Selection strategy (TWAS). Firstly, the data set is divided into several disjoint sample blocks to get the global well-distributed anchor candidate. Then, the algorithm proceeds to select anchor points within each local candidate anchor set. This two-stage anchor selection strategy facilitates the identification of anchors with significant representativeness at a reduced computational expense, thereby adeptly capturing the intrinsic data structure. Secondly, the present study devises an adaptive near-neighbor graph learning approach to construct an anchor-based intra-view similarity matrix. Finally, the multiple views are fused to obtain a consistent inter-view similarity matrix, and the clustering results are obtained. Extensive experiments demonstrate the effectiveness, efficiency, and stability of the TWAS algorithm.
光谱聚类因其良好的聚类效果而备受关注,但其高昂的计算成本使其难以应用于大规模多视角聚类。针对这一问题,本文提出了一种简单高效的大规模多视角光谱聚类算法,该算法基于两阶段分布良好的锚点选择策略(TWAS)。首先,将数据集划分为多个不相邻的样本块,以获得全局分布良好的候选锚点。然后,算法继续在每个局部候选锚点集中选择锚点。这种两阶段锚点选择策略有助于识别具有显著代表性的锚点,同时降低计算成本,从而有效地捕捉数据的内在结构。其次,本研究设计了一种自适应近邻图学习方法,用于构建基于锚点的视图内相似性矩阵。最后,融合多个视图以获得一致的视图间相似性矩阵,并得出聚类结果。大量实验证明了 TWAS 算法的有效性、高效性和稳定性。
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引用次数: 0
Physical information-guided multidirectional gated recurrent unit network fusing attention to solve the Black-Scholes equation 物理信息引导的多向门控递归单元网络融合注意力求解布莱克-斯科尔斯方程
IF 2.9 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-10-11 DOI: 10.1016/j.dsp.2024.104766
Zhaoyang Zhang, Qingwang Wang, Yinxing Zhang, Tao Shen
Reasonable option pricing is crucial in the financial derivatives market. Finding analytical solutions for the Black-Scholes (BS) equation, particularly for American options or with fluctuating volatility and interest rates, is challenging. BS equations exhibit strong time-series characteristics, with asset prices typically adhering to geometric Brownian motion. To address the BS equations, we propose a sequence-to-sequence model guided by physical information (PI), called PiMGA. The PiMGA fuses a multidirectional gated recurrent unit (GRU) network with an attention module, where multidirectional GRU enhances the coding performance of the input sequences and the attention module balances the feature weights of the hidden variables. Prior physical knowledge in BS equations is jointly used as a constraint, forming the penalty function for objective optimization. This allows PiMGA to serve as an efficient approximation function in the learning paradigm of physically informed machine learning to solve BS equations. BS equations with various complexities illustrate the accuracy and feasibility of PiMGA for numerical solutions. Furthermore, the out-of-distribution generalization ability of PiMGA is verified by predicting the Nasdaq 100 index.
合理的期权定价对金融衍生品市场至关重要。为布莱克-斯科尔斯(Black-Scholes,BS)方程(尤其是美式期权或波动率和利率波动的期权)寻找分析解具有挑战性。BS 方程具有很强的时间序列特征,资产价格通常遵循几何布朗运动。为了解决 BS 方程问题,我们提出了一种以物理信息(PI)为指导的序列到序列模型,称为 PiMGA。PiMGA 融合了多向门控递归单元(GRU)网络和注意力模块,其中多向门控递归单元增强了输入序列的编码性能,而注意力模块则平衡了隐藏变量的特征权重。BS 方程中的先验物理知识被共同用作约束条件,形成目标优化的惩罚函数。这使得 PiMGA 成为物理信息机器学习范式中的高效近似函数,用于求解 BS 方程。不同复杂程度的 BS 方程说明了 PiMGA 数值求解的准确性和可行性。此外,还通过预测纳斯达克 100 指数验证了 PiMGA 在分布外的泛化能力。
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引用次数: 0
Enhancing RODNet detection in complex road environments based on ESM and ISM methods 基于 ESM 和 ISM 方法加强复杂道路环境中的 RODNet 检测
IF 2.9 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-10-10 DOI: 10.1016/j.dsp.2024.104816
Yu Guo, Yaxin Xiao, Yan Zhou, Yanyan Li, Siyu Yang, Chuangrui Meng
In autonomous driving, accurately identifying traffic targets is crucial for ensuring the safe and reliable operation of autonomous vehicles. Millimeter-wave radar, known for its low cost, long detection range, and excellent performance under various weather conditions. Deep learning algorithms, particularly the radar object detection network (RODNet), have been effectively applied to radar target detection by analyzing the range-azimuth (RA) heatmaps that capture complex target features. However, the low angular resolution of radar RA heatmaps, combined with the high sensitivity of millimeter-wave radar to metal objects, makes adjacent targets prone to misdetection and increases the likelihood of misclassification of target types due to metal reflections from road obstacles. To address these issues, this paper proposes an innovative extension suppression method to enhance RA heatmaps, reducing interference between adjacent targets and significantly improving target resolution. Additionally, the paper incorporates Gaussian filtering, peak detection, and amplitude suppression algorithms to design an interference suppression method, accurately identifying and mitigating strong reflections from non-target regions, thereby improving detection efficiency in complex environments. The effectiveness and superiority of these methods have been fully validated, with AP improvements of 18% in overlapping scenarios, 2% in metal obstacle scenarios, and around 10% in high-speed scenarios compared to the latest methods.
在自动驾驶中,准确识别交通目标是确保自动驾驶汽车安全可靠运行的关键。毫米波雷达以其成本低、探测距离远、在各种天气条件下性能优异而著称。深度学习算法,特别是雷达目标检测网络(RODNet),通过分析捕捉复杂目标特征的测距方位(RA)热图,已被有效地应用于雷达目标检测。然而,雷达方位角热图的角度分辨率较低,再加上毫米波雷达对金属物体的高灵敏度,使得相邻目标容易被误检,并增加了因道路障碍物的金属反射而导致目标类型分类错误的可能性。为解决这些问题,本文提出了一种创新的扩展抑制方法来增强 RA 热图,减少相邻目标之间的干扰,并显著提高目标分辨率。此外,本文还结合高斯滤波、峰值检测和振幅抑制算法设计了一种干扰抑制方法,可准确识别和减轻来自非目标区域的强反射,从而提高复杂环境下的探测效率。这些方法的有效性和优越性已得到充分验证,与最新方法相比,在重叠场景中 AP 提高了 18%,在金属障碍物场景中提高了 2%,在高速场景中提高了约 10%。
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引用次数: 0
FusionNGFPE: An image fusion approach driven by non-global fuzzy pre-enhancement framework FusionNGFPE:非全局模糊预增强框架驱动的图像融合方法
IF 2.9 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-10-10 DOI: 10.1016/j.dsp.2024.104801
Xiangbo Zhang , Gang Liu , Mingyi Li , Qin Ren , Haojie Tang , Durga Prasad Bavirisetti
The majority of prevailing image fusion methods employ a global strategy, often resulting in a reduction of contrast. This study addresses this issue by proposing a novel image fusion approach called FusionNGFPE, specifically designed for the structural characteristics of infrared (IR) imagery. The approach introduces a contrast equalization algorithm based on the Fourth-order Partial Differential Equation (FPDE) to enhance background regions effectively. Considering the inherent differences between IR and visible (VIS) images, we developed a hybrid fusion strategy that combines the Expectation Maximization (EM) algorithm and Principal Component Analysis (PCA). Comparative analysis with state-of-the-art fusion methods shows that our proposed algorithm achieves superior performance in both qualitative and quantitative evaluations. To further demonstrate the practical significance of FusionNGFPE, we integrated this fusion framework into the RGBT target tracking task using the VOT-RGBT and OTCBVS datasets. Extensive comparative experiments confirm that the FusionNGFPE framework integrates seamlessly with the tracking task, significantly improving tracking accuracy across diverse scenarios.
大多数流行的图像融合方法都采用全局策略,这往往会导致对比度降低。针对这一问题,本研究提出了一种名为 FusionNGFPE 的新型图像融合方法,专门针对红外图像的结构特征而设计。该方法引入了基于四阶偏微分方程(FPDE)的对比度均衡算法,以有效增强背景区域。考虑到红外图像与可见光(VIS)图像之间的固有差异,我们开发了一种混合融合策略,该策略结合了期望最大化(EM)算法和主成分分析(PCA)。与最先进的融合方法进行的比较分析表明,我们提出的算法在定性和定量评估中都取得了优异的性能。为了进一步证明 FusionNGFPE 的实际意义,我们使用 VOT-RGBT 和 OTCBVS 数据集将该融合框架集成到 RGBT 目标跟踪任务中。广泛的对比实验证实,FusionNGFPE 框架与跟踪任务无缝集成,显著提高了不同场景下的跟踪精度。
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引用次数: 0
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Digital Signal Processing
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