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feature-based detection and classification of moving objects using LiDAR sensor

IET INTELLIGENT TRANSPORT SYSTEMS 2019年第7期13卷 1088-1096页

作者： Guo, Ziming Cai, Baigen Jiang, Wei Wang, Jian Beijing Jiaotong Univ Sch Elect & Informat Engn 3 Shangyuancun Beijing Peoples R China Beijing Jiaotong Univ Beijing Engn Res Ctr EMC & GNSS Technol Rail Tran 3 Shangyuancun Beijing Peoples R China Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety 3 Shangyuancun Beijing Peoples R China

detection and classification of moving objects is essential for autonomous driving. To tackle this problem, this paper proposes an object classification method at detection level using a single LiDAR sensor. The aim is to extract and classify all the moving vehicles, bicyclists, and pedestrians in front of the sensor. First, the point clouds are segmented to produce distinct groups of points representing different objects, where the line segments are extracted. A segmentation combination strategy is conducted to address the over-segmentation caused by occlusion. Then, considering the object geometry and reflection intensity, several features for classification are defined and extracted from different hand-labelled object classes. Reference ranges of all the features are generated on a set of experiment samples. Finally, the object class can be decided by checking if its features match with the existing feature reference ranges of a certain class. The proposed method was evaluated using datasets gathered by the vehicle demonstrator, and the experiment results show considerable improvement of classification performance compared to an existing object classification method based on the object matching framework.

关键词： object detection image segmentation image classification optical radar image matching feature extraction vehicle demonstrator object matching framework feature-based detection autonomous driving single LiDAR sensor moving vehicles object geometry reflection intensity object classification point cloud segmentation feature reference ranges hand-labelled object class feature-based classification

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feature-based detection of automated language models: tackling GPT-2, GPT-3 and Grover

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PEERJ COMPUTER SCIENCE 2021年 7卷 e443页

作者： Froehling, Leon Zubiaga, Arkaitz Leibniz Univ Hannover Hannover Germany Queen Mary Univ London London England

The recent improvements of language models have drawn much attention to potential cases of use and abuse of automatically generated text. Great effort is put into the development of methods to detect machine generations among human-written text in order to avoid scenarios in which the large-scale generation of text with minimal cost and effort undermines the trust in human interaction and factual information online. While most of the current approaches rely on the availability of expensive language models, we propose a simple feature-based classifier for the detection problem, using carefully crafted features that attempt to model intrinsic differences between human and machine text. Our research contributes to the field in producing a detection method that achieves performance competitive with far more expensive methods, offering an accessible "first line-of-defense" against the abuse of language models. Furthermore, our experiments show that different sampling methods lead to different types of flaws in generated text.

关键词： feature-based detection Language models Language generation Text classification NLP

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Phase-feature-based detection of Small Targets in Sea Clutter

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2022年 19卷 1页

作者： Xie, Jianda Xu, Xiaojian Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China

It is known that the challenge for detection of small targets on the sea surface is the low signal-to-clutter ratio (SCR). In particular, for low grazing angles and high sea states, wave shading and sea spikes make the small target detection even more difficult. This letter proposed a novel phase-feature detector of small targets in sea clutter. Three phase features, which correspond to different radar scattering mechanisms between the target and sea surface, are extracted in the phase domain of radar echoes, namely, the number of phase crossing zero points, the maximum value of the phase difference probability density function, and the decorrelation time of the phase difference. A detector with a controllable false alarm rate (FAR) based on phase features is constructed using the fast convex hull learning algorithm. Experimental results on measured databases demonstrate that the proposed phase-feature detector attains better performance than the existing tri-feature detectors.

关键词： Clutter feature extraction Radar Detectors Radar clutter Radar cross-sections Object detection Fast convex hull learning algorithm feature-based detection phase features sea clutter small target detection

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Sea Surface Floating Small Target detection based on a Priori feature Distribution and Multiscan Iteration

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IEEE JOURNAL OF OCEANIC ENGINEERING 2025年第1期50卷 94-119页

作者： Xu, Shuwen Zhang, Tian Ru, Hongtao Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China McMaster Univ Hamilton ON L8S 4L8 Canada

To address the issue that the detection performance of conventional sea target detectors deteriorates seriously in short accumulated pulses, this article designs a feature detection method based on a priori feature distribution and multiscan iteration, which enhances the feature extraction ability of existing feature-based detection methods. The initial step involves the utilization of kernel density estimation for the purpose of fitting the a priori feature distribution model. Subsequently, the original feature vectors of the current scan are iterated based on the a priori feature distribution model to obtain improved feature vectors. After the feature iteration of the current scan is completed, the original feature vectors of the current scan are incorporated into the historical features to generate a new distribution model. The improved feature vectors after iteration are employed for training the decision region and detecting targets by the convex hull algorithm. The proposed method is designed to enhance the stability and reliability of detection features, thereby facilitating a greater degree of separation between the extracted features of sea clutter and target returns within the feature space. The measured IPIX data sets and Naval Aviation University X-Band data sets demonstrate that the proposed method can effectively improve the detection performance of existing multifeature-based detection methods in scenarios involving short accumulated pulses.

关键词： feature extraction Detectors Clutter Doppler effect Radar Vectors Radar detection Object detection Sea surface Radar clutter A priori feature distribution feature-based detection kernel density estimation (KDE) multiscan iteration sea clutter

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Graph-based Maximum Connected-Component Learning Algorithm for Small Target detection in Maritime Radars

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2025年第1期61卷 250-265页

作者： Bai, Xiaohui Xu, Shuwen Guo, Zixun Shui, Penglang Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China Northwestern Polytech Univ Sch Elect & Informat Xian 710072 Peoples R China

Anomaly detection needs to learn one-class classifiers from normal instances in observation or feature spaces. In the Neyman-Pearson criterion, the design of one-class classifiers boils down to finding the minimal-volume decision region subject to the error probability of normal instances no larger than a desired false alarm rate. The theoretical solution to this design problem is the probability density function (pdf) level set of normal instances. In low-dimensional feature spaces, by combining training samples with the convexity regularity on decision regions, the convexhull learning algorithm is a technique for designing one-class classifiers. In order to overcome its dimension limitation and the mismatch of convexity to the level sets of a multimodal pdf, this article considers the approach to replace the convexity by the connectivity to regularize decision regions. A fast graph-based maximum connected-component learning algorithm is proposed to design one-class classifiers in high-dimensional feature spaces, which exploits the fast maximum connected-component search algorithm in a large-scale undirected graph. Moreover, for the application of sea-surface small target detection, the proposed algorithm combines ten-dimensional features to design feature-based detectors. Experimental results on the recognized radar database indicate the effectiveness of the proposed algorithm.

关键词： Training Detectors Level set feature extraction Classification algorithms Signal processing algorithms Radar Anomaly detection convexhull learning algorithm feature-based detection graph-based maximum connected-component (GMCC) learning algorithm sea-surface small target detection

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detection of Small Targets in Sea Clutter Using Dual-Polarization Correlation features and One-Class Classifier

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： Liu, Lichao Guo, Qiang Huang, Shuai Kaliuzhnyi, Mykola Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Peoples R China Kharkiv Natl Univ Radio Elect Sci & Res Lab UA-61166 Kharkiv Ukraine

Due to the nonstationary, time-varying, and target-like characteristics of sea clutter, detection of small targets embedded within it has been a long-standing and formidable challenge in the field of remote sensing. Traditional adaptive detectors based on sea clutter modeling often struggle to achieve satisfactory detection performance. To address this issue, this letter proposed a novel small target detector embedded in sea clutter that leverages dual-polarization correlation features and one-class classifier. Initially, three distinct features are extracted from the significant differences between the target echoes and sea clutter in the dual-polarization correlation domain. Each individual feature possesses a certain level of discriminative power. Furthermore, under the framework of anomaly detection, a one-class classifier for small sea surface target detection is constructed based on the fast convex hull learning (FCHL) algorithm to make the final decisions. Experimental results based on the IPIX datasets demonstrate that the proposed detector outperforms several existing detectors.

关键词： Clutter feature extraction Detectors Correlation Object detection Eigenvalues and eigenfunctions Sea surface Radar Time series analysis Scattering feature-based detection one-class classifier polarization fusion sea clutter small target detection

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Target detection in Sea Clutter based on feature Re-Expression Using Spearman's Correlation

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IEEE SENSORS JOURNAL 2024年第19期24卷 30435-30450页

作者： Guan, Jian Jiang, Xingyu Liu, Ningbo Ding, Hao Dong, Yunlong Liu, Tong Naval Aviat Univ Inst Informat Fus Yantai 264000 Peoples R China Shandong Commun & Media Coll Jinan Peoples R China

The target detection method based on multidimensional feature space is commonly used for detecting small maritime targets in sea clutter. However, existing methods do not fully explore and utilize the correlation information between features, limiting their detection performance. To address this limitation, this article proposes a target detection method in sea clutter based on feature re-expression using Spearman's correlation. First, a generalized linear grouping method based on Spearman's correlation is proposed to strengthen the linear relationships within groups while reducing intergroup feature correlations. Second, intragroup feature re-expression based on Bhattacharyya distance (BD) is introduced, yielding new features with superior classification capabilities and complementary informational relationships. Third, a tri-feature concave hull detection algorithm based on dual distance weights is designed to enhance detection performance while controlling the false alarm rate. Testing with measured data from three public datasets demonstrates that the proposed detection method exhibits significant improvements in detection performance compared to existing feature-based detection methods.

关键词： feature extraction Radar Clutter Object detection Radar detection Radar cross-sections Correlation feature-based detection sea clutter sea-surface small target detection

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Priori Information-based feature Extraction Method for Small Target detection in Sea Clutter

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷

作者： Wu, Xijie Ding, Hao Liu, Ningbo Dong, Yunlong Guan, Jian Naval Aviat Univ Yantai 264001 Peoples R China

Under the framework of feature-based detection of small targets on sea surface, existing feature extraction methods only use the echo data of current frame while ignoring the influence of historical echo data. Nevertheless, due to the nonstationarity of sea clutter, it may lead to unstable extraction of detection features and then affect detection performance. To solve this problem, this article designs a feature extraction method based on a priori information for small target detection. It first obtains a priori information from historical echo data by the kernel density estimation (KDE) method. Then, the corresponding feature estimation method is utilized to obtain improved features according to the relationship between current frame data and a priori information. Finally, the feature information of current frame is integrated into a priori information to prepare the next feature extraction. The measured data are utilized to verify the performance of the proposed method and the results reveal that this method can effectively improve detection performance, especially when sea clutter and target echo have good separability. In addition, the complexity of algorithm is analyzed to prove that the proposed method has certain application potential.

关键词： feature extraction Clutter Radar Time-frequency analysis Sea measurements Sea surface Object detection A priori information feature-based detection kernel density estimation (KDE) sea clutter

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Sea-Surface Floating Small Target detection by One-Class Classifier in Time-Frequency feature Space

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2018年第11期56卷 6395-6411页

作者： Shi, Sai-Nan Shui, Peng-Lang Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Shaanxi Peoples R China

This paper presents one feature-based detector to find sea-surface floating small targets. In integration time of the order of seconds, target returns exhibit time-frequency (TF) characteristics different from sea clutter. The normalized smoothed pseudo-Wigner-Ville distribution (SPWVD) is proposed to enhance TF characteristics of target returns, which is computed from the SPWVDs of time series at the cell under test (CUT) and reference cells around the CUT. The differences between target returns and the TF pattern of sea clutter are congregated on the normalized SPWVD. From that the ridge integration (RI) is computed and significant TF points from each time slice form a binary image. The number of connected regions and the maximum size of connected regions in the binary image are extracted and are combined with the RI into a 3-D feature vector. Due to the unavailability of the feature vector samples of radar returns with target, a one-class classifier with a controllable false alarm rate is constructed from the feature vector samples of sea clutter by the fast convex hull learning algorithm. As a result, a new feature-based detector is designed. It is compared with the tri-feature-based detector using amplitude and Doppler features and the fractal-based detector using the Hurst exponent of amplitude time series on the recognized IPIX radar database for floating small target detection. The results show that a significant improvement in detection performance is attained.

关键词： Fast convex hull learning algorithm feature-based detection floating small target detection normalized smoothed pseudo-Wigner-Ville distribution (SPWVD) one-class classifier

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High accuracy flashlight scene determination for shot boundary detection

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SIGNAL PROCESSING-IMAGE COMMUNICATION 2003年第3期18卷 203-219页

作者： Heng, WJ Ngan, KN Nanyang Technol Univ Sch Comp Engn Singapore 2263 Singapore Inst Infocomm Res Singapore Singapore Univ Western Australia Dept Elect & Elect Engn Nedlands WA 6009 Australia

Shot boundary detection, or scene change detection, is a technique used in the initial phase of video indexing. One of the problems in the detection is the discrimination of abrupt scene change from flashlight scenes. The usual discriminate method tests the similarity of the frame before and after a suspected flashlight effect. However, the performance of such a technique in discriminating flashlight scene from abrupt scene change can be affected by the scene content. To overcome this, we present a novel method that utilises the edge direction, thereby reducing erroneous matching with increasing dilation radius. This improves the accuracy of similarity testing and reduces the amount of erroneously matched edges by four times. Our experiment in discriminating flashlight effect from abrupt scene change frame pairs shows that our technique produces a perfect detection, which cannot be achieved by normal edge-based detection. Such a contribution is important as it improves the indexing of real life video. (C) 2003 Elsevier Science B.V. All rights reserved.

关键词： shot boundary detection flashlight detection abrupt scene change detection feature-based detection directed edges matched edge elimination

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