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检索条件"任意字段=Proceedings of the 1999 Signal and Data Processing of Small Targets"

共 904 条记录，以下是11-20 订阅

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Improved pedestrian detection algorithm of Yolov5 in dense scenes

Improved pedestrian detection algorithm of Yolov5 in dense s...

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2024 International Conference on Image, signal processing, and Pattern Recognition, ISPP 2024

作者： Lin, Chuanwen Liang, Jiaxing School of Artificial Intelligence and Big Data Hefei University Hefei China Institute of Dataspace Hefei China

ISBN: (纸本)9781510680425

Occluded, small-scale pedestrians are easy to occur in dense scenes to solve this problem, an improved Yolov5 is proposed detection algorithm. First of all, feature extraction for small-size pedestrians is insufficient the problem uses SPD-Conv convolution to enhance the complex background small target feature extraction ability. Second, fuse an efficient multi-foot the degree of attention module further enhances the visual area of the target pedestrian feature extraction. Finally, focal eiou loss was used as the loss letter number, so that the regression more attention to high-quality samples, improve the accuracy of the model and robustness. Training and testing were performed on the dataset CrowdHuman in the test, the improved Yolov5 algorithm AP50 can reach 83.6 %, phase for the original algorithm, AP50 and AP50−95 are improved by 2.5 %, respectively. Average accuracy improved by 2.2%. The experimental results validate the proposed method effectiveness in crowded scenarios. © 2024 SPIE.

关键词： Detection and tracking algorithms Object detection Convolution Performance modeling Feature extraction small targets data modeling Evolutionary algorithms Target detection Statistical modeling

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AugTarget data Augmentation for Infrared small Target Detection 48

AugTarget Data Augmentation for Infrared Small Target Detect...

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48th IEEE International Conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Chen, Shengjia Zhu, Jiewen Ji, Luping Pan, Hongjun Xu, Yuhao University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Sichuan University School of Computer Science and Engineering Chengdu610065 China

ISBN: (纸本)9781728163277

Sample shortage has always been a frequently-faced problem for the machine-learning models in infrared small target detection. As one of main limitations, it is hampering the further promotion of target detection performance. In this paper, we propose a simple and effective data augmentation scheme, AugTarget, to address this shortage issue of small target samples. Our scheme mainly consists of two crucial algorithms: target augmentation and batch augmentation. The former is designed to generate sufficient targets, by random target representation. The latter is devised to diversify training samples. Moreover, the initially-generated image samples of small targets are further enriched by randomly aggregating the feature representation of different images. The experiments on public datasets demonstrate that our AugTarget could bring an obvious improvement to mean intersection over union (mIoU). Cooperated by the augmentation of our AugTarget, the state-of-the-art (SOTA) method, AGPC could even achieve a distinct performance promotion by 3.03%, 2.17% and 2.63% on MDFA, SIRST-Aug and Merged datasets, respectively. In addition, the experimental results on three baseline models also show the universality & adaptivity of AugTarget to different dataset augmentation. Our source codes are available at https://***/UESTC-nnLab/AugTarget. © 2023 IEEE.

关键词： data augmentation Infrared small target detection Target & batch augmentation

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A Reinforcement Learning Agent Controlled Multi-branch small Object Detection Framework

A Reinforcement Learning Agent Controlled Multi-branch Small...

引用

2025 IEEE International Conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Hong, Junkun Long, Yitian Luo, Yueyi Hua, Liujie Long, Jun Qi, Qianqian School of Computer Science and Engineering Central South University Changsha China Data Science Institute Vanderbilt University NashvilleTN United States School of Mathematics and Statistics Central South University Changsha China Big Data Institution Central South University Changsha China

ISBN: (纸本)9798350368741

The past few years have witnessed the immense development of small object detection, which is aimed at detecting size-limited targets in high-resolution images. The prevailing methods focus on extracting fine-grained information by expanding the receptive fields and then generating the potential small object region. However, these solutions inevitably add sophisticated detectors and extra learning components, which incur time-consuming and computation-costing. Meanwhile, we observe that it's suboptimal to extract fine features in such a generic way. To alleviate the issues, we propose a multi-branch small object detection framework with a regular-scale detection branch and a small-scale detection branch. Specifically, we design and pre-train a reinforcement learning agent to control feature extractors in both branches according to the results of small object areas. Moreover, we present a region clipping algorithm to rebuild the small object to regular size, which can be input into a mature detector directly. The extensive experiments on COCO, VisDrone, SODA-D, and our collecting TVDS datasets demonstrate our method outperforms the state-of-the-art methods in several metrics. © 2025 IEEE.

关键词： multi-branch small object detection region clipping region discriminator reinforcement learning agent

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Sea Surface small Target Detection using Tri-median Normalized Doppler Spectrum Feature 16

Sea Surface Small Target Detection using Tri-median Normaliz...

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16th International Conference on signal processing Systems, ICSPS 2024

作者： Guo, Zi-Xun Wu, Kang-Hui Bai, Xiao-Hui Su, Jia Fan, Yi-Fei Gong, Yan-Yun Wang, Ling Shui, Peng-Lang School of Electronics and Information Northwestern Polytechnical University Xi’an710072 China School of Information Xi’an University of Finance and Economics Shaanxi Xi’an710100 China The National Laboratory of Radar Signal Processing Xidian University Xi’an710071 China

ISBN: (纸本)9781510689251

This letter aims to develop a detector for detecting small targets on the sea surface based on feature utilization median normalization. At the long observation time, sea clutter and returns with target show differences of Doppler amplitude spectra (DAS). The median normalized DAS (MNDAS) is used to enhance the Doppler characteristics of returns with target, which is computed from the DAS of the cell under test and its neighboring reference cells. From the MNDAS, three salient features can be extracted for the detection, including the non-zero interval length, vector entropy and global accumulation in the MNDAS. The three features are combined into 3D feature vectors. Considering that the returns with target and their feature vectors cannot be used in the training, one semi-supervised classifier based on the convexhull learning algorithm is built by the feature vectors of sea clutter only. Finally, a new detector based on feature utilization median normalization which can control the false alarm rate precisely is constructed. Through the experiments on the IPIX and CSIR radar databases and one offshore experimental data using an unmanned aerial vehicle with a corner reflector, the proposed detector attains competitive performance and lower computation cost with the TF-tri-feature-based detector. © 2025 SPIE.

关键词： Vectors

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A training method to improve the effect of small target detection

A training method to improve the effect of small target dete...

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2022 International Conference on Image, signal processing, and Pattern Recognition, ISPP 2022

作者： Chen, Boxu Yu, Lixin Meng, Xiao Hua, Yang Beijing Microelectronic Technology Institute Beijing100076 China

ISBN: (纸本)9781510654846

Deep learning target detection has always been a major research direction in the field of artificial intelligence. Its research results are widely used in the fields of automatic driving, security system and medical treatment. This paper proposes a method to improve the detection effect of small targets, which realizes the detection of objects of different scales in the input image, especially to improve the detection effect of small-scale targets. Before the collected data set is sent to the neural network for training, it is first divided into three different scales according to the size of the target to be detected in the image of the data set. Then one or several images in the large target data set are stitched, the images in the small target data set are enlarged, and the above two types of images are used to form a new data set. Finally, the new data set and the original data set are sent to the neural network for training. In this paper, the YOLOX target detection network is used for verification. The results show that the detection effect of the network obtained by this method on small targets has been improved, and the missed detection rate has been reduced from 31.2% to 27.5%. At the same time, the detection effect of large and medium-sized targets has not been sacrificed. © 2022 SPIE.

关键词： Image enhancement

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Research on the Enhancement Method of small Moving targets with Low signal-to-Noise Ratio

Research on the Enhancement Method of Small Moving Targets w...

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Conference on AOPC - Optical Sensing, Imaging, and Display Technology

作者： Li, Kun Piao, Yupeng Qian, Weixian Nanjing Univ Sci & Technol Sch Elect & Opt Engn Nanjing 210094 Jiangsu Peoples R China Shanghai Aerosp Control Technol Inst 1555 Zhongchun Rd Shanghai 201109 Peoples R China

ISBN: (纸本)9781510661905;9781510661912

small moving targets with low signal-to-noise ratio have the characteristics of weak intensity and small size. For stationary targets, it can be enhanced by the time-domain multi-frame accumulation algorithm. However, for small moving targets, the traditional direct multi-frame accumulation method cannot effectively enhance the target due to the change of the position of the target between frames and the spread of target energy. Based on this feature, this paper proposes an enhancement method based on uniform moving small targets. Because the target moves at a uniform speed between frames, the target smear image can be obtained by directly accumulating multiple frames of the collected target image sequence according to the stationary target, and then according to the smear information, the real moving speed of the target can be reversed. Finally, according to this speed, the small target image sequence is shifted and accumulated by multiple frames. In order to verify the effectiveness of the proposed method, the experiment is designed to simulate the motion of a small target by carrying a faint point light source on a motorised slide at constant speed, collecting data for correlation processing. The experimental results show that the method proposed in this paper can quickly and accurately estimate the speed of the small target moving between frames. The accumulation of small target images shifted according to this speed can significantly enhance the target and improve the signal-to-noise ratio.

关键词： Time domain multi-frame accumulation low signal-to-noise ratio target trailing detection target enhancement

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Remote sensing aircraft small object detection algorithm based on YOLOv5

Remote sensing aircraft small object detection algorithm bas...

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2024 International Conference on Image, signal processing, and Pattern Recognition, ISPP 2024

作者： Qiu, Yijuan Xue, Jiefeng Zhang, Jie Jiang, Ping Zhang, Gang Lei, Tao National Key Laboratory of Optical Field Manipulation Science and Technology Chinese Academy of Sciences Sichuan Chengdu610209 China Institute of Optics and Electronics Chinese Academy of Sciences Sichuan Chengdu610209 China University of Chinese Academy of Sciences Beijing100049 China Southwest University of Science and Technology 621010 China

ISBN: (纸本)9781510680425

With the rapid development of remote sensing technology, remote sensing images play an important role in the agricultural field, geological field, and natural disaster detection. The size of aircraft in complex scenes in remote sensing images is extremely small, and aircraft of different models have similar shapes. Therefore, improving the accuracy of aircraft target recognition is a challenging task. We propose an improved aircraft small target recognition method based on yolov5, which can improve the recognition accuracy of aircraft targets while ensuring the speed of the model. The specific content is as follows: To address the problem of lack of remote sensing aircraft training sets, we use existing public remote sensing images to combine with aircraft model images. Most of the aircraft models only occupy a dozen to twenty pixels in the 1k*1k image, and perform Scale the generated data set;in order to better combine features of different scales and obtain higher-level feature fusion, introduce the BiFPN module with more residual connections and more complete feature fusion;use the SE attention mechanism to learn the weights of different features, extract information that is more important for detection and improve model performance;in view of the small size of the aircraft, a detection method based on Wasserstein distance such as NWD (Normalized Wasserstein Distance) is selected as the loss function. © 2024 SPIE.

关键词： Training aircraft

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Imaging Detection and Classification for Near-space Dim targets 7

Imaging Detection and Classification for Near-space Dim Targ...

引用

7th International Conference on Vision, Image and signal processing, ICVISP 2023

作者： Yuan, Yupu Xi, Runping Chen, Qian School of Computer Science Northwestern Polytechnical University Xi'an China Shaanxi Province Key Laboratory of Speech & Image Processing Xi'an China National Engineering Laboratory of Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi'an China

ISBN: (纸本)9781839539848

The motion mode of near-space targets is complex due to their high threat level. The target imaging faces low SNR and susceptibility to background noise. The existing detection and classification algorithms struggle to meet the demands of performance and speed on such targets. To address these issues, a YOLOv5-based target detection algorithm utilizes CARAFE upsampling and the small detection head to extract internal features and edge information of dim target. The algorithm adeptly captures dim target features, enhancing real-time performance and detection accuracy while overcoming subtle feature granularity and extraction difficulties. Aiming at the problem that there is no existing trajectory classification algorithm in the near-space field, we propose an infrared dim target trajectory classification algorithm. The improved attention module is used to enhance the extraction of motion features, and the target category is accurately identified by the prediction module, improving the capacity to extract dependencies between trajectory fragments. We execute comparative experiments on common infrared dim target dataset, near-space infrared dim target dataset and real dataset to confirm the viability of our methods. The outcomes indicate that our proposed method can be applied to the above three types of dim targets and significantly outperforms state-of-the-art methods. © The Institution of Engineering & Technology 2023.

关键词： Extraction

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AN AUTONOMOUS FEATURE DETECTION METHOD FOR SLOW small targets ON SEA SURFACE BASED ON CONTEXTUAL BANDIT APPROACH

AN AUTONOMOUS FEATURE DETECTION METHOD FOR SLOW SMALL TARGET...

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IET International Radar Conference 2023, IRC 2023

作者： Wu, Xijie Liu, Tianpeng Liu, Yongxiang Liu, Li Xu, Jianfeng Zhu, Jing National University of Defense Technology Changsha410005 China Rocket Force University of Engineering Xi'an710025 China Unit 92192 of the PLA China

ISBN: (纸本)9781839539954

This paper combines the contextual bandit approach with existing feature detection process, and then proposes an autonomous feature detection method of slow small targets on sea surface based on contextual bandit approach. The method is divided into the training stage and detection stage. In training stage, the method utilizes a large amount of sea clutter data collected in advance to generate the decision regions corresponding to existing feature detectors. In detection stage, every time the radar receives a frame of data, the method selects a feature detector in real time through the modified kernel-based upper confidence bound policy proposed in this paper. Finally, the decision region of real-time detector is used to detect the target in current frame. The superior performance of proposed method is verified by the measured sea clutter datasets. © The Institution of Engineering & Technology 2023.

关键词： Radar clutter

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Holographic Radar Tracking Algorithm for "Low-Slow-small" Maneuvering targets

Holographic Radar Tracking Algorithm for "Low-Slow-Small" Ma...

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IET International Radar Conference 2023, IRC 2023

作者： Niu, Guo Zhu, Nannan Fang, Xueqing Lei, Xueyue Wang, Yifan Zhong, Fuli Foshan University Foshan China Sun Yat-Sen Univiersity Guangzhou China Jinan University Guangzhou China Guangzhou Communications Technician Institute Guangzhou China

ISBN: (纸本)9781839539954

The proliferation of unauthorized flights and incidents involving "low-slow-small" maneuvering targets presents an increasing threat to national security and the normal progression of the national economy. Effectively addressing the threat has emerged as a vital task in upholding the security of the nation, society, and economy. This paper focuses on the attributes of "low-slow-small" targets and leverages the characteristics of holographic radar echoes to propose a Doppler-based Interaction Multiple Model Multiple Hypothesis Tracking (DIMM-TOMHT) algorithm. This algorithm yields the following advantages: 1) The tracking gate constructed by a single prediction step of the DIMM-TOMHT algorithm encompasses dimensions of Doppler velocity components. Owing to distinct disparities in position and velocity between targets and clutter, the multi-dimensional tracking gate containing Doppler components eliminates numerous irrelevant measurements, thereby reducing computational burden of the tracking algorithm while enhancing the accuracy of correlation and precision of target tracking. 2) To attain optimal global hypothesis trajectories, the DIMM-TOMHT algorithm computes scores for each hypothetical trajectory iteratively, which is employed for confirming transient trajectories and eliminating old trajectories. The algorithm inherently integrates aspects of trajectory management, thereby mitigating the complexity of multi-object tracking systems. Empirical data validate the efficacy of this algorithm in tracking "low-slow-small" targets. © The Institution of Engineering & Technology 2023.

关键词： Object tracking

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