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An enhance ranging algorithm based on multi-waveform classification with hyperspectral LiDAR

MEASUREMENT 2025年 253卷

作者： Li, Xingyun Shao, Hui Lu, Yuan Chen, Yuwei Sun, Long Dai, Hui Hong, Tao Zhang, Xinyuan Zhang, Hulong Anhui Jianzhu Univ Anhui Int Joint Res Ctr Ancient Architecture Intel Hefei 230601 Peoples R China Infrared & Low Temp Plasma Key Lab Anhui Prov Hefei 230037 Peoples R China Adv Laser Technol Lab Anhui Prov Hefei 230037 Peoples R China Anhui Jianzhu Univ Anhui Prov Engn Res Ctr Reg Environm Hlth & Spatia Hefei 230601 Peoples R China

When the light detection and ranging (LiDAR) detected system interrogates targets with high retro-reflective properties, the backscattered pulse energy is prone to exceed the maximum receiving range of the photodetector. It will cause signal saturation because the system cannot record the complete echo waveform, which leads to the deviation of ranging results. To solve the problem, this study proposes an enhanced ranging algorithm based on multi-waveform classification (RMWC) with 101-band hyperspectral LiDAR (HSL). Considering the difference of echo waveforms collected in different bands, the threshold method is combined with random forest classifier to classify echo waveforms into normal waveform, saturated waveform and U-shaped waveform. Then peak value method (PV), centroid method (CM) and waveform fitting method (WF) are selected to calculate the corresponding time of flight of the three waveforms respectively. Finally, the target distance is determined by the weighted average of the mode values of the bands' ranging results. Compared with PV, CM and WF ranging results at 905 nm, the experimental results verify that the proposed algorithm can effectively reduce the ranging error caused by waveform saturation. For planar retro-reflective target, the optimal average error (AE) and standard deviation (Std) are 0.0053 m and 0.0083 m, respectively. For diffuse reflection whiteboard, the AE is 0.0010 m and the Std is 0.0053 m. The reconstruction results of planar target point cloud show that RMWC ranging results are better than these traditional single-band ranging methods, which can provide a reference for the optimal design of high precision laser ranging system.

关键词： Laser ranging Saturated waveform Multi-waveform classification Waveform time discrimination

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Pneumatic Piping Leakage Flow Rate classification Based on Transfer Learning

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IEEE SENSORS JOURNAL 2025年第10期25卷 17811-17822页

作者： Shi, Yan Chang, Jiaqi Li, Lei Ma, Yushan Wang, Yixuan Xu, Shaofeng Niu, Yanxia Ma, Dai Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Liupanshan Lab Yinchuan 750000 Peoples R China Beihang Univ Engn Practice & Innovat Ctr Beijing 100191 Peoples R China Ningxia Univ Sch Mech Engn Yinchuan 750021 Peoples R China Zhejiang Easun Pneumat Sci & Technol Co Ltd Ningbo 315000 Peoples R China

Leakage is the most common form of malfunction in pneumatic systems. When leakage occurs, the escaping gas creates a cooler area near the leakage point. Thermal imaging can visually capture these temperature variations, allowing assess the severity of the pneumatic system's leakage. However, the accuracy of thermal imaging is often affected by varying background temperatures due to differing image feature distributions. To solve this problem, this article introduces a framework that combines key area extraction with discriminative feature transfer learning (KDFTL). First, to meticulously extract the core area of the leakage from the expanse of the image environment, the infrared images from both the source and target domains (TDs) are fed into the key area extraction (KAE) section. Subsequently, a distribution difference index is designed on class centers, and the inter class separability and intra class compactness are evaluated. Then, to make the features of samples with the same leakage rate at different temperatures similar for classification, the feature transfer matrix is calculated and the transferred features are obtained and classified. Finally, extensive experiments on the dataset of thermal images for pneumatic leakage show that KDFTL delivered an average classification accuracy of 72.59%, which is a significant improvement compared to 44.07% transfer joint matching (TJM).

关键词： Feature extraction Transfer learning Pneumatic systems Accuracy Sensors Intelligent sensors YOLO Temperature distribution Temperature sensors Image color analysis Leakage detection pneumatic system target detection transfer learning (TL)

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Small infrared target detection by data-driven proposal and deep learning-based classification 44

Small infrared target detection by data-driven proposal and ...

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Conference on infrared technology and Applications XLIV

作者： Ryu, Junhwan Kim, Sungho Yeungnam Univ Dept Elect Engn 280 Daehak Ro Gyongsan 38541 Gyeongsangbuk D South Korea

ISBN: (纸本)9781510617605

In this paper, we propose a data-driven proposal and deep-learning based classification scheme for small target detection. Previous studies have shown feasible performance using conventional computer vision techniques such as spatial and temporal filters. However, those are handcrafted approaches and are not optimized due to the nature of the application fields. Recently, deep learning has shown excellent performance for many computer vision problems, which motivates the deep learning-based small target detection. The proposed data-driven proposal and convolutional neural network (DDP-CNN) can generate possible target locations through the data-driven proposal and final targets are recognized through the classification network. According to the experimental results using drone database, the DDP-CNN shows 91% of train accuracy and 0.85 of average precision (AP) of the target detection.

关键词： Small target detection infrared small target detection Deep learning classification Region proposal Convolution Neural Network

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Study on efficient recognition and accurate localization method of waste plastic bottles based on deep learning

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ECOLOGICAL INFORMATICS 2025年 86卷

作者： Xie, Shilong Wu, Hu Mao, Wenjie Chu, Xianlong Meng, Yixing Yang, Xianhai Shandong Univ Technol Sch Mech Engn Zibo 255000 Peoples R China

As a vital component of ecologically sustainable development, the effective recovery and reuse of waste plastic bottles is essential for environmental protection and resource recycling. Given the varying recycling values of plastic bottles based on their colors, precise sorting and recycling are particularly important. Traditional manual sorting methods face challenges such as low efficiency and high costs. In contrast, machine vision-based image recognition technology offers a more efficient solution for classifying and recovering waste plastic bottles, with classification recognition and target positioning being critical technologies for the optimal use of ecological resources. This study introduces a deep learning approach for identifying and locating waste plastic bottles, utilizing the reversible column network (RevCol) as the backbone to prevent information loss. A lightweight combined decoupling head is designed to minimize computational load while enhancing accuracy. The Weighted Intersection over Union version 3 (WIoU v3) loss function is incorporated to improve detection performance. By leveraging depth information from an infrared camera alongside RGB image mapping, the method achieves recognition and three-dimensional positioning. Experimental results indicate that the proposed model outperforms traditional models, with a 36.39 % reduction in parameters and a 50.62 % decrease in computational requirements, while accuracy and recall rates improve by 4.56 % and 12.14 %, respectively. Additionally, mAP50 and mAP50-95 values increase by 5.86 % and 3.89 %, and the recognition speed reaches 62 FPS, a 51.22 % improvement, meeting real-time detection needs. Experiments conducted on a deep learning-UR5 robot platform demonstrate high recognition accuracy and sorting success rates in actual waste plastic bottle sorting scenarios. The promotion and implementation of this method will significantly enhance the recycling of waste plastic resources and contribute to th

关键词： Recycling of waste plastic bottles Machine vision Deep learning target recognition classification detection Three-dimensional positioning

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Single frame IR point target detection based on a Gaussian mixture model classification

Single frame IR point target detection based on a Gaussian m...

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Conference on Electro-Optical and infrared Systems - technology and Applications IX

作者： Genin, Laure Champagnat, Frederic Le Besnerais, Guy Astrium Satellites F-31402 Toulouse 4 France Onera French Aerosp Lab F-91761 Palaiseau France

ISBN: (纸本)9780819492821

This paper deals with point target detection in non-stationary background such as cloud scenes in aerial or satellite imaging. We propose a new method to estimate second order background statistics within a spatial detection method by Matched Filter (MF). Our approach consists in classifying the pixels of the image by gathering the pixels of similar covariance matrices and in estimating one covariance matrix for each class. We use a classification Expectation-Maximization (CEM) algorithm based on a zero mean Gaussian mixture model. A key issue is the robustness of the classification with regard to target presence. This problem is efficiently tackled in a very simple manner. The resulting Gaussian Mixture Matched Filter (GMMF) has the advantage to adapt itself to second order non-stationary backgrounds and requires only the tuning of the window size used to build the observation vector associated with each pixel. Efficiency of the proposed GMMF approach is demonstrated on a large variety of cloudy sky backgrounds.

关键词： Point target detection Gaussian Mixture Model Image Segmentation Matched Filter

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Integrating hyperspectrograms with class modeling techniques for the construction of an effective expert system: Quality control of pharmaceutical tablets based on near-infrared hyperspectral imaging

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JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS 2025年 256卷 116697页

作者： Pieszczek, Lukasz Daszykowski, Michal Univ Siles Katowice Inst Chem 9 Szkolna St PL-40006 Katowice Poland Univ Siles Katowice Ctr Microscop Studies Matter SPIN Lab 75 Pulku Piechoty St 1 PL-41500 Chorzow Poland

Near-infrared hyperspectral imaging (NIR-HSI) integrated with expert systems can support the monitoring of active pharmaceutical ingredients (APIs) and provide effective quality control of tablet formulations. However, existing quality control methods usually test a limited number of variability sources affecting the final product. This study examines the potential of NIR-HSI (in the spectral range of 935.61-1720.2 nm) as an advanced and high-throughput detector to identify different manufacturing factors and their fluctuations that impact tablet properties. These are, for instance, particle sizes of powdered excipients, their mixing, compression force used to form a tablet, origin of ingredients, storage conditions, and concentration of API. During the study, the novel expert system approach was developed to support NIR-HSI, enabling the detection of subtle, diverse substandard anomalies in tablets. The system combines (i) hyperspectrograms, which characterize and simplify tablet spatial heterogeneity through principal component analysis scores distribution, and (ii) a one-class classifier (OCC), trained exclusively on target class samples, without the need for substandard tablets. The system was trained to recognize known sources of variation and validated using tablets with cellulose, magnesium stearate, and ascorbic acid as API. It outperformed the alternative approach based on averaged spectra, achieving 100.00% sensitivity and 98.77% specificity.

关键词： Near-infrared hyperspectral imaging (NIR-HSI) Process analytical technology (PAT) Pharmaceutical quality control Hyperspectrogram One-class classification

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ASSK-Net: Automatic Spatial Selection Kernel Network for Faster Oriented Object detection

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JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2025年 1-14页

作者： Hu, Yong Jing, Zhengbiao Su, Yun Zhang, Fang Jing, Donglin Chengdu Vocat & Tech Coll Ind Sch Informat Engn Chengdu Peoples R China Chengdu Vocat & Tech Coll Ind Sch Intelligent Mfg & Automot Chengdu Peoples R China Shanghai Aerosp Control Technol Inst Shanghai 201109 Peoples R China China Aerosp Sci & Technol Corp Res & Dev Ctr Infrared Detect Technol Shanghai 201109 Peoples R China

The improvement in existing remote sensing detectors often relies on complex structures and deeper convolutional layers, leading to a significant increase in computational complexity. While previous works have designed various novel lightweight convolutions to improve target feature representation, these structures often overlook the unique prior knowledge of remote sensing scenes when applied to aerial detection tasks, resulting in decreased detection performance. Specifically, this prior knowledge includes: (1) Accurate detection of aerial objects often requires extensive contextual information. (2) The range of contextual information needed for different categories varies greatly. To address this, our paper fully considers this prior knowledge and proposes the Adaptive Spatial Selection Kernel Network (ASSK-Net). We first use large kernel convolution decomposition operations to decouple a series of convolutions with smaller sizes and rapidly expanding dilation rates, constructing diverse receptive fields with different spatial coverage ranges while significantly reducing computation. Based on this, a spatial selection mechanism is used to effectively weight feature maps with different levels of information richness, enhancing the feature differences between targets and backgrounds. Finally, we design an adaptive class equilibrium loss function, increasing the loss weight with rare categories and other easily confused categories with similar classification scores, thus improving the discriminative capability for rare categories. Comprehensive experiments on datasets (such as DOTA, UCAS-AOD, and HRSC2016) indicate that our ASSK-Net achieves optimal performance, effectively balancing computational complexity and detection accuracy.

关键词： Lightweight network Oriented object detection Convolution decomposition Sample balance

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Real-time multi-sensor data fusion for target detection, classification, tracking, counting, and range estimates

Real-time multi-sensor data fusion for target detection, cla...

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Conference on detection and Remediation Technologies for Mines and Minelike targets IX

作者： Tsui, EK Thomas, R U.S. Army Tank-Automotive and Armaments Command/Armament Research Development and Engineering Ctr. (United States) McQ Associates Inc. (United States)

ISBN: (纸本)0819453382

As part of the Commanding General of Army Material Command's Research, Development & Engineering Command (RDECOM), the U.S. Army Research Development and Engineering Center (ARDEC), Picatinny funded a joint development effort with McQ Associates, Inc. to develop an Advanced Minefield Sensor (AMS) as a technology evaluation prototype for the Anti-Personnel Landmine Alternatives (APLA) Track III program. This effort laid the fundamental groundwork of smart sensors for detection and classification of targets, identification of combatant or noncombatant, target location and tracking at and between sensors, fusion of information across targets and sensors, and automatic situation awareness to the 1(st) responder. The efforts have culminated in developing a performance oriented architecture meeting the requirements of size, weight, and power (SWAP). The integrated digital signal processor (DSP) paradigm is capable of computing signals from sensor modalities to extract needed information within either a 360degrees or fixed field of view with acceptable false alarm rate. This paper discusses the challenges in the developments of such a sensor, focusing on achieving reasonable operating ranges, achieving low power, small size and low cost, and applications for extensions of this technology.

关键词： Sensors Cameras target detection Imaging systems infrared cameras Image processing infrared imaging Image compression Digital signal processing Image resolution

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infrared aircraft few-shot classification method based on cross-correlation network

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红外与毫米波学报 2025年第1期44卷 103-111页

作者： HUANG Zhen ZHANG Yong GONG Jin-Fu Key Laboratory of Infrared System Detection and Imaging Technology Chinese Academy of SciencesShanghai 200083China University of Chinese Academy of Sciences Beijing 100049China Shanghai Institute of Technical Physics Chinese Academy of SciencesShanghai 200083China

In response to the scarcity of infrared aircraft samples and the tendency of traditional deep learning to overfit,a few-shot infrared aircraft classification method based on cross-correlation networks is *** method combines two core modules:a simple parameter-free self-attention and *** analyzing the self-correlation and cross-correlation between support images and query images,it achieves effective classification of infrared aircraft under few-shot *** proposed cross-correlation network integrates these two modules and is trained in an end-to-end *** simple parameter-free self-attention is responsible for extracting the internal structure of the image while the cross-attention can calculate the cross-correlation between images further extracting and fusing the features between *** with existing few-shot infrared target classification models,this model focuses on the geometric structure and thermal texture information of infrared images by modeling the semantic relevance between the features of the support set and query set,thus better attending to the target *** results show that this method outperforms existing infrared aircraft classification methods in various classification tasks,with the highest classification accuracy improvement exceeding 3%.In addition,ablation experiments and comparative experiments also prove the effectiveness of the method.

关键词： infrared imaging aircraft classification few-shot learning parameter-free attention cross attention

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detection and classification of target formations in infrared image sequences

Detection and classification of target formations in infrare...

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Imaging infrared: Scene Simulation, Modeling, and Real Image Tracking

作者： Bronskill, John F. Hepburn, John S.A. Au, Wing K. Advanced Technology Cent Canada

This paper describes a knowledge-based approach to the detection and classification of ground-based military target formations in infrared (IR) image sequences at long range from an airborne platform. The motivation for this approach is that at long range military vehicle targets in IR imagery typically consists of only a few pixels, have low contrast and exist in a high clutter environment making reliable detection, classification or identification of individual targets extremely difficult. Thus, the system detailed here detects targets based on their membership in a military target formation or convoy, relying on the structure or spatial organization of the formation and not individual target signatures. The system consists of two major components;Point target detection and Tracking (PTD);and Knowledge-Based Constraint Hierarchical target Formation detection and classification (TFD). The first component detects and tracks both stationary and moving point targets which are passed on as candidates for clustering into target formations by a rule-based matching process in the second component.

关键词： infrared Imaging

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