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红外技术 2025年第1期47卷 81-88页

作者：邱刚张廼龙柏仓谭笑陈杰高嵩国网江苏省电力有限公司电力科学研究院江苏南京211103 国网江苏省电力有限公司江苏南京210000

在电网、桥梁等较为复杂的工况环境下,巡检机器人可以有效代替人工进行设备的检测与维护。为了实现巡检机器人在复杂环境中的自动避障作业,提出了基于YOLOv5深度学习网络模型的红外与可见光图像匹配识别技术,使机器人能够识别并分类各... 详细信息

在电网、桥梁等较为复杂的工况环境下,巡检机器人可以有效代替人工进行设备的检测与维护。为了实现巡检机器人在复杂环境中的自动避障作业,提出了基于YOLOv5深度学习网络模型的红外与可见光图像匹配识别技术,使机器人能够识别并分类各种障碍物,包括活体和非活体障碍。在实际巡检作业中,机器人同时配置红外相机和普通相机实时监测其前方环境。基于大量数据的训练后的YOLOv5网络模型,机器人能够快速、准确识别并判断前方障碍物并进行分类,平均识别精度高达99.2%。机器人不仅能够识别障碍物的性质,还能采取相应的主动动作来应对不同情况。实验结果充分证明了这种基于多种图像信息的综合避障方法的有效性。机器人能够在不同场景中检测、分类和规避障碍物,从而提高了其自主性和适应性。该技术在自动化巡检、安全监测以及救援任务等领域具有广泛的应用前景,为机器人技术的不断发展提供了有力支持。

关键词：自动化避障红外目标检测 YOLOv5 深度学习

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infrared point target detection based on multi-label generative MRF model

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infrared PHYSICS & technology 2017年 83卷 188-194页

作者： Wang, Longguang Lin, Zaiping Deng, Xinpu Natl Univ Def Technol Coll Elect Sci & Engn Changsha 410073 Hunan Peoples R China

infrared point target detection based on markov random field (MRF) is mainly formulated as a binary classification problem, leading to a poor adaptability to complex background with high false alarm rate. This paper formulates the infrared point target detection as a multi-classification problem, and proposes a detection method based on multi-label generative MRF (MG-MRF) model. First, the MG-MRF model is proposed and the optimal label configuration of the infrared image is derived using iterated condition mode (ICM). Second, the pointwise adaptive filter is structured utilizing local labels to suppress the background clutter. Finally, an adaptive threshold is utilized to segment the target in the residual image. The experimental results on various backgrounds demonstrate that the detection method based on MG-MRF has a strong suppression of false alarm with superior performance in terms of accuracy and efficiency. (C) 2017 Elsevier B.V. All rights reserved.

关键词： infrared point target detection Markov random field Generative MRF model Background clutter suppression

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Multi-Modal Stream Focusing Salient Object detection based on Visible-infrared Complementary Fusion

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IEEE Transactions on Instrumentation and Measurement 2025年

作者： Zhang, Yujin Gao, Haoyi Sohel, Ferdous Wu, Fei Muzahid, A.A.M. Zhao, Jingwen Du, Zhendong Zhang, Lijun Shanghai University of Engineering Science School of Electronic and Electrical Engineering Shanghai201620 China Murdoch University School of Information Technology Perth6150 Australia Shanghai Technology and Innovation Vocational College School of Intelligent Manufacturing Shanghai201620 China

Cross-modal fusion of visible-infrared images can make targets more prominent, the interaction between multi-modal stream fusion and salient object detection tasks can more accurately depict the target. We propose a multi-modal stream focusing on a salient object detection network based on visible-infrared complementary fusion, namely MFCF. MFCF has two main subnetworks: an Attentional Complementary Image Fusion subnetwork for Light Perception (AComFusion) and a Multimodal Stream Focusing Contextual Salient Object detection (MSFCSod).To address the issue where redundant information across modalities weakens the fusion, AComFusion is designed with an attention mutual information complementary module to remove redundancy and enhance complementary advantages. Additionally, a light classification module performs adaptive classification of lighting conditions, adjusting the contribution weights of modalities to obtain optimal quality under various lighting conditions. The output of AComFusion is used as a third modality stream and input into MSFCSod along with the visible and infrared sources. This fusion stream drives and guides the detection of infrared and visible streams to externally focus on significant target features. An efficient focusing amplifier module is designed to internally self-focus on the detected significant targets, enhancing their feature representations. Finally, the contextual fusion module integrates more low-level details and high-level semantic features to improve the texture edges of the objects, thus enhancing the MFCF network. Thorough experimental results on several benchmark datasets show that the proposed MFCF network achieved state-of-the-art performance. It also shows strong potential in the subtasks of image fusion and salient object detection. © 1963-2012 IEEE.

关键词： Thermography (imaging)

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基于YOLOv8算法的稀土熔盐电解槽炉面温度监测研究

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有色金属（冶炼部分） 2025年第1期 84-91页

作者：侯伟黄金堤李明周李静江西理工大学信息工程学院江西赣州341000 江西理工大学冶金工程学院江西赣州341000 高性能钢铁合金材料江西省重点实验室江西赣州341000

电解温度与稀土熔盐电解槽电流效率、炉体寿命紧密相关,然而热电偶测温、红外热成像等测量方法受电解车间高温强腐蚀环境影响难以实时检测。基于YOLOv8算法对熔盐电解槽炉面温度进行预测。首先,通过高温试验炉自制温度数据集并基于YOLOv... 详细信息

电解温度与稀土熔盐电解槽电流效率、炉体寿命紧密相关,然而热电偶测温、红外热成像等测量方法受电解车间高温强腐蚀环境影响难以实时检测。基于YOLOv8算法对熔盐电解槽炉面温度进行预测。首先,通过高温试验炉自制温度数据集并基于YOLOv8算法训练获得温度区间分类模型;其次,采用图像灰度与温度关系式重建炉面图像温度云图;最后,基于YOLOv8-SSW算法构建了炉面温度图像识别模型,其预测准确率为93.4%,可用于电解槽炉面温度监测。

关键词：稀土熔盐电解槽温度检测 YOLOv8 目标分类

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EGISD-YOLO: Edge Guidance Network for infrared Ship target detection

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2024年 17卷 10097-10107页

作者： Zhan, Weida Zhang, Cong Guo, Shufang Guo, Jinxin Shi, Mingkai Changchun Univ Sci & Technol Sch Elect Informat Engn Changchun 130013 Peoples R China Baicheng Meteorol Bur Baicheng 137000 Peoples R China

In the marine field, infrared detection technology is of great significance for timely localization and detection of ships in security missions. However, since infrared ship targets are often in the environmental conditions of small pixel occupancy, low contrast and complex background, it poses a great challenge for multiship detection, classification, and localization tasks. Therefore, to address these problems we propose an edge information-guided infrared ship target detection (EGISD-YOLO) network, in which a dense-csp structure is designed to improve the csp module of YOLO to increase the reusability of the backbone feature information, and in addition, to address the noise and interference generated by the image in the complex background, a deconvolutional channel attention module (DCA) is designed to link the contextual language to the image, which relates the contextual semantics to obtain the local information of the target. Crucially, we propose an edge-guided structure that takes the edge information of low-level features as a cue to fuse with deep-level features to enrich the target contour and thus improve the target localization ability, so that the network still possesses robustness under low-contrast conditions, and finally, we add a small-size prediction head at the end of the network to further increase the detection ability of weak targets. The proposed EGISD-YOLO is experimentally demonstrated to have better detection performance for infrared ship targets.

关键词： Attention mechanism edge guidance infrared target detection ship image

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infrared dim target detection method inspired by human vision system

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OPTIK 2020年 206卷 164167-000页

作者： Li, Shaoyi Li, Chenhui Yang, Xi Zhang, Kai Yin, Jianfei Northwestern Polytech Univ Sch Astronaut Xian 710072 Peoples R China Shanghai Acad Spaceflight Technol Shanghai 201109 Peoples R China

infrared dim target detection has long been a key technology for various systems, such as infrared search and track (IRST) systems and the Space-Based infrared System (SBIRS). However, it is difficult for traditional detection methods to adapt to different types of complex backgrounds. Therefore, this paper proposes an adaptive infrared dim target detection method based on human visual contrast, motion, prediction, and other characteristics. First, according to the characteristics of different types of background images, the classification preprocessing strategy is adopted to remove noise, suppress the background, and improve the target signal-to-noise ratio. Second, on the basis of the visual contrast and scale adaptation mechanism, we propose an adaptive multi-scale local contrast method to extract the saliency region, and we then analyze the spectral scale to further suppress the background, enhance the target central area, and construct a suspected target set. Finally, the candidate moving target set is obtained by motion region matching using the optical flow method, and a multi-frame screening strategy combined with dynamic pipeline filtering is proposed to identify the target and reduce the false positive rate. Our experiment results indicate that the proposed method can adapt to changes in the target scale and achieve stable and adaptive detection of dim targets in the background of sky, sea-sky, and ground objects.

关键词： infrared dim target target detection Scale adaptation Visual contrast Pipeline filtering

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infrared Image classification and detection Algorithm for Power Equipment Based on Improved YOLOv10

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IEEE ACCESS 2024年 12卷 184976-184988页

作者： Ji, Xiu Yue, Zheyu Yang, Hongliu Zhang, Zehong Changchun Inst Technol Natl Local Joint Engn Res Ctr Smart Distribut Grid Changchun 130000 Peoples R China State Grid Tonghua Power Supply Co Tonghua 134000 Jilin Peoples R China

Real-time target detection has important applications in industry. Identifying the type of power equipment in massive infrared image data for heat fault diagnosis has emerged as a crucial issue that needs to be addressed in this field. However, infrared imaging technology has shortcomings such as poor signal clarity and serious background noise interference. To address this problem, this paper proposes an infrared image classification and detection algorithm for power equipment based on the improved YOLOv10, named YOLOv10plus. Firstly, a lightweight convolution technique (GSConv) is implemented within the central network system to maximize the retention of target features. Secondly, a Slim Neck design structure is used in the neck network and combined with a dual convolution module (DualConv) to achieve a lightweight model. Finally, a Multi Cavity Channel Refiner (MDCR) module is implemented at the foundational level of the network to enhance the target feature recognition. To better handle the imbalance in sample distribution, the original Binary Cross-Entropy (BCE) loss function is modified to incorporate Focal Loss, aiming to enhance model performance by focusing more on hard-to-classify examples. The experimental findings indicate that the enhanced model achieves a substantial improvement in detection performance on power equipment infrared image data. Its FPS value reaches 588 and mAP value reaches 85.3%, which are 361 and 13.3% higher than the original model, respectively.

关键词： Feature extraction Convolution Accuracy Computational modeling YOLO Convolutional neural networks Real-time systems infrared imaging classification algorithms Neck infrared imaging of power equipment improvement of YOLOv10 Slim Neck-GSConv MDCR

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Multi-band composite detection and recognition of aerial infrared point targets

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infrared PHYSICS & technology 2018年 94卷 102-109页

作者： Kou, Tian Zhou, Zhongliang Liu, Hongqiang Yang, Yuanzhi Air Force Engn Univ Aeronaut Engn Coll Xian 710038 Shaanxi Peoples R China

Because of the low detection probability and recognition efficiency of the single-band signal of an infrared point target, the multi-band composite detection probability is analyzed and a multi-band recognition method is proposed. By using the binary hypothesis method, we can conveniently deduce the multi-band theoretical detection threshold and obtain the composite detection probability and false alarm probability. The numerical simulation results show that multi-band composite detection ability is superior to that of single-band. For the difficult classification between background and target spectral signals with serious interference noise in reality, this paper proposes a highly effective spectral signal recognition method based on the optimized AdaBoost algorithm. By virtue of the high classification accuracy of the AdaBoost algorithm, with the Pruning classifier as a basic classifier to be learnt, the proposed method abandons any abnormal data that seriously deviate from the mean value center of the correction classification data, the end result of which reduces the number of iterations and enhances the classification effect of the AdaBoost algorithm. Compared with other algorithms from test results, the optimized AdaBoost algorithm has higher recognition accuracy and quicker computation, and it reduces the sensitivity to noise to prevent the phenomenon of over-fitting.

关键词： Multi-band signal detection probability Recognition method Optimized AdaBoost learning

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infrared Point target detection with Fisher Linear Discriminant and Kernel Fisher Linear Discriminant

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JOURNAL OF infrared MILLIMETER AND TERAHERTZ WAVES 2010年第12期31卷 1491-1502页

作者： Liu, Ruiming Zhi, Hongliang Huaihai Inst Technol Sch Elect Engn Lianyungang 222005 Jiangsu Peoples R China Jiangsu Automat Res Inst Lianyungang 222006 Jiangsu Peoples R China

It is a challenging task to detect point targets from an infrared image. Recently, the pattern recognition theory has been used to detect targets. The principal component analysis (PCA) has gained success in this field. We propose a linear subspace detection method based on Fisher linear discriminant (FLD) in this paper. If we consider images are made up of target class data and background class data, the target detection problem can be translated into a two-class classification problem. The FLD as one of pattern recognition algorithms can be used to find potential targets from image background. After classification by FLD, a map function, Gaussian map function, is developed to generate detection images in which the larger target-to-background contrast is obtained. FLD is a linear detection method without taking the higher-order statistics of image data into account. To improve detection performance, we extend this detection method to its nonlinear version, kernel FLD (KFLD) detection. Because the nonlinear subspace is capable of capturing the part of higher-order statistics, the better detection performance can be achieved. The well-devised experiments verify that KFLD detection outperforms FLD and other common used detection methods.

关键词： target detection infrared image PCA FLD Kernel method

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Strategies for detecting organic liquids on soils using mid-infrared reflection spectroscopy

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ENVIRONMENTAL SCIENCE & technology 2008年第15期42卷 5700-5705页

作者： Gallagher, Neal B. Gassman, Paul L. Blake, Thomas A. Eigenvector Res Inc Manson WA 98831 USA Pacific NW Natl Lab Richland WA 99352 USA

Stand-off monitoring for chemical spills can provide timely information for cleanup efforts, and mid-infrared reflection spectroscopy is one approach being investigated for spill detection. Using laboratory data, anomaly and target detection strategies were examined for the detection of four different low volatility organic liquids on two different soil types. Several preprocessing and signal-weighting strategies were studied. Anomaly detection for C-H bands was good using second derivative preprocessing and provided similar performance to that of target detection approaches such as generalized least-squares and partial least-squares, with detections at soil loads of approximately 3-6 mu g/cm(2) areal dosage. Good performance was also found for the detection of P=O, O-H, and C=O stretching vibrational modes, but the optimal strategy varied. The simplicity and generality of anomaly detection is attractive;however, target detection provides more capability for classification.

关键词： Waste spills Chemical spills Liquids Organic compounds Soils infrared spectroscopy Chemical accidents Reflectance spectroscopy Soil classification

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