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Foggy day target image detection based on Radon transform and feature space decision

JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS 2025年

作者： Li, Dongmei Yang, Yanting Liu, Guangyu Liu, Biao Zhao, Enming Zhou, Weiyun Dali Univ Sch Engn Dali Peoples R China Shanghai Jiao Tong Univ Marine Intelligent Equipment & Syst Lab Shanghai Peoples R China Chongqing Yitong Coll Sch Commun & Informat Engn Chongqing Peoples R China

As global urbanization rapidly progresses, the dramatic increase in urban population and the number of motor vehicles has intensified traffic safety issues, especially under adverse weather conditions such as heavy fog. These conditions significantly diminish vehicles' environmental perception capabilities and drivers' visibility, while the effect of light scattering further increases the risk of traffic accidents, drawing extensive attention in the field of public safety. To address the issues of low contrast, insufficient accuracy, and high false detection rates in traditional foggy-day target detection technologies, this study introduces a novel foggy-day target image detection model based on Radon transform and feature space decision-making. The model preprocesses the image to extract edge information, simplifying the target's initial representation. It then segments the target image and applies a normalized grayscale Hough transform to enhance clarity and contrast of the target trajectory. After Radon transformation and inverse Radon transformation for image reconstruction, peak points are extracted and matched with peaks from the wavelet transform for precise localization. Finally, the extracted parameters are input into the feature space for decision-making, achieving accurate detection of foggy-day target images. Experimental results demonstrate that the proposed method significantly improves image clarity, reduces detection errors, and enhances target trail detail retention. Further evaluation using structural similarity, peak signal-to-noise ratio, root mean square error, mean absolute error, standard deviation of mean error, naturalness image quality evaluator, and figure of merit validated the effectiveness and robustness of the method. This study confirms the superior performance of the proposed method and provides a new direction for target image detection under adverse weather conditions.

关键词： feature space decision foggy day target image image detection Radon transform trace contrast

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An Improved BP Algorithm for Tumor image detection 3rd

An Improved BP Algorithm for Tumor Image Detection

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3rd World Conference on Intelligent and 3D Technologies, WCI3DT 2024

作者： Li, Xiang Gong, Junyi Ni, Xiang Lv, Zheng Bethune First Hospital of Jilin University Changchun China

ISBN: (纸本)9789819791231

Tumor image detection is an important technology in the field of medical imaging, which aims to accurately identify and locate the presence of tumors through the analysis and processing of images and provide an important basis for clinical diagnosis and treatment. In today’s society, there is a problem of low accuracy of tumor image detection, which greatly affects the efficiency and accuracy of tumor image detection. The BP algorithm in neural network is an effective tumor image detection technique. In this paper, an image detection system is created by using the BP algorithm, which greatly improves the accuracy of tumor image detection and improves the efficiency of tumor image detection. Finally, the experimental results show that the BP algorithm is easy to operate and the accuracy rate reaches 98.4%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： BP algorithm Efficiency image detection Neural network Tumor

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image detection Using Combinatorial Auction

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF SIGNAL PROCESSING 2024年 5卷 1015-1022页

作者： Anuk, Simon Bendory, Tamir Painsky, Amichai Tel Aviv Univ Sch Elect Engn IL-6997801 Tel Aviv Israel Tel Aviv Univ Ind Engn Dept IL-6997801 Tel Aviv Israel

This paper studies the optimal solution of the classical problem of detecting the location of multiple image occurrences in a two-dimensional, noisy measurement. Assuming the image occurrences do not overlap, we formulate this task as a constrained maximum likelihood optimization problem. We show that the maximum likelihood estimator is equivalent to an instance of the winner determination problem from the field of combinatorial auction and that the solution can be obtained by searching over a binary tree. We then design a pruning mechanism that significantly accelerates the runtime of the search. We demonstrate on simulations and electron microscopy data sets that the proposed algorithm provides accurate detection in challenging regimes of high noise levels and densely packed image occurrences.

关键词： Combinatorial auction gap statistics image detection winner determination problem Combinatorial auction gap statistics image detection winner determination problem

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image detection Based-On the Analysis of Interpolating Signal Periodicity

Image Detection Based-On the Analysis of Interpolating Signa...

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7th International Conference on MEMS, NANO and Smart Systems (ICMENS 2011)

作者： Zhu, Jueyu Chen, Mengyuan Li, Feng Hunan First Normal Univ Dept Informat Sci & Engn Changsha 410205 Hunan Peoples R China Changsha Univ Sci & Technol Sch Comp & Telecommun Changsha 410114 Peoples R China

ISBN: (纸本)9783037853122

A novel algorithm is introduced in this paper which gives the spectrum characteristics of an image by analyzing the periodicity of interpolating signal and then estimate scaling ratio of the image. In this paper we also compare the spectrum characteristics of each block after partitioning an image into small overlapping blocks for judging whether the image was spliced. Moreover, tampered regions can be identified. Experiment results show that this algorithm can detect whether a photograph captured by the digital camera is undergone the digital zoom. At the same time it gives the estimation of scaling ratio. As well as, it is efficient to detecting the re-sampled image.

关键词： image Interpolation Scaling Ratio Fast Fourier Transform image detection

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Antifaces: A novel, fast method for image detection

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2001年第7期23卷 747-761页

作者： Keren, D Osadchy, M Gotsman, C Univ Haifa Dept Comp Sci IL-31905 Haifa Israel Technion Israel Inst Technol Dept Comp Sci IL-32000 Haifa Israel

This paper offers a novel detection method, which works well even in the case of a complicated image collection-for instance, a frontal face under a large class of linear transformations. It is also successfully applied to detect 3D objects under different views. Call the collection of images, which should be detected, a multitemplate. The detection problem is solved by sequentially applying very simple filters (or detectors), which are designed to yield small results on the multitemplate thence, "antifaces"), and large results on "random" natural images. This is achieved by making use of a simple probabilistic assumption on the distribution of natural images, which is borne out well in practice. Only images which passed the threshold test imposed by the first detector are examined by the second detector, etc. The detectors are designed to act independently so that their false alarms are uncorrelated;this results in a false alarm rate which decreases exponentially in the number of detectors. This, in turn, leads to a very fast detection algorithm. Typically, (1 + delta )N operations are required to classify an N-pixel image, where delta < 0.5. Also, the algorithm requires no training loop. The algorithm's performance compares favorably to the well-known eigenface and support vector machine based algorithms, but is substantially faster.

关键词： image detection smoothness distribution of natural images rejectors

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Integrating knowledge-based sparse representation for image detection

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NEUROCOMPUTING 2021年 442卷 173-183页

作者： Lu, Guangxi Tian, Ling Zheng, Xu Hui, Bei Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Sichuan Peoples R China Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu 610054 Sichuan Peoples R China

image detection has been a primary step and widely adopted in diverse domains, such as autopilot, risk inspection, and robot vision. The main body of current models for image detection treat this task as the feature-based recognition of multiple independent objects. The adoption of semantic relationships, which can provide auxiliary information for joint recognition, is still in its early stage. Inspired by deep dictionary learning, this work proposes a novel framework named SEGNN, which aims at finding and using the sparse representation knowledge to improve the result of image detection. According to this design, both the efficiency and rationality of reasoning over semantic relationships are well captured. The framework includes an improved GGNN model. It can efficiently extract the semantic informations from the image, and map it to sparse vector. The framework also applies a novel SimRank method, to justify the rationality of the semantic reasoning. The performance of the framework is validated extensively on two datasets Visual Genome (VG) and COCO. The experimental results and case study have demonstrated the effectiveness of the proposed framework. (c) 2021 Published by Elsevier B.V.

关键词： image detection Deep reasoning Knowledge graph Gated graph neural network Simrank Dictionary learning

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Improved YOLOv5-based image detection of cotton impurities

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TEXTILE RESEARCH JOURNAL 2024年第7-8期94卷 906-917页

作者： Hu, Daojie Liu, Xiangjun Xu, Jian Donghua Univ Coll Mech Engn Shanghai Peoples R China Xian Polytech Univ Coll Elect & Informat Xian Peoples R China 4 Coll Bldg 2999 Renmin North Rd Shanghai 201620 Peoples R China

Aiming at the embedded devices with limited resources and the characteristics of small and scattered distribution of cotton impurities, which cause the problems of low degree of accuracy and slow speed during detection, we propose the GBM-YOLOv5 lightweight cotton impurities detection model. First, the open-source software LabelImg was used to classify and label the targets in order to construct a dataset of cotton mottled images. Second, because of the lightweight structure of the Ghost module, the original CSP structure was replaced by the GhostBottleneck composed of the Ghost module, which effectively reduced the number of parameters and computation of the model. After the Ghost module, finally, a SoftPool structure was introduced in the SPP module to improve the pooling operation and retain more detailed feature information. Final results validate that the average detection accuracy of GBM-YOLOv5 was improved by 2.59% compared to YOLOv5 and 5.79% compared to YOLOv4 by training different network models on the well-constructed dataset. The model can meet the practical requirements of cotton industrial production and better improve the purity of cotton.

关键词： image detection cotton hash Convolutional Block Attention Module attention mechanism SoftPool structure GBM-YOLOv5

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Unlocking Signal Processing With image detection: A Frequency Hopping detection Scheme for Complex EMI Environments Using STFT and CenterNet

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IEEE ACCESS 2023年 11卷 46004-46014页

作者： Chen, Ziyi Shi, Yaowu Wang, Yingwei Li, Xinbo Yu, Xiaohui Shi, Yiran Jilin Univ Coll Commun Engn Changchun 130022 Peoples R China

Accurate detection and parameter estimation of frequency hopping (FH) signals remain challenging in FH signal-based transmission systems. This study proposes a scheme combining time-frequency analysis (TFA) and deep learning (DL)-based image processing algorithms to alleviate the degradation of detection accuracy and estimation performance caused by complex electromagnetic interference (EMI). A short-time Fourier transform (STFT) was used to obtain the signal spectrogram, which reflects the signal energy in a concentration-dependent manner. Then, a CenterNet-based deep network was employed to identify each FH hop's shape and position, reducing the computational burden via a lightweight neural network while maintaining high recognition accuracy. Inverse mapping from the coordinates to the spectrogram was used to perform parameter estimation in the time-frequency (TF) domain. The estimation error was reduced by precisely locating the centroid of the signal energy using CenterNet. The simulation results demonstrate that the proposed scheme can accurately estimate the FH signal at a low signal-to-noise ratio (SNR) with complex EMI. Furthermore, appropriately determining the optimal parameters of CenterNet to ensure the estimator performance provides a novel approach for integrating DL into signal detection and estimation in complex EMI environments.

关键词： Spectrogram Electromagnetic interference Feature extraction Time-frequency analysis Signal detection Computational modeling Time-domain analysis FH signal TFA CenterNet complex EMI image detection

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Neck posture monitoring system based on image detection and smartphone sensors using the prolonged usage classification concept

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2018年第10期13卷 1501-1510页

作者： Lawanont, Worawat Inoue, Masahiro Mongkolnam, Pornchai Nukoolkit, Chakarida Shibaura Inst Technol Syst Engn & Sci Minuma Ku 307 Fukasaku Saitama Saitama 3378570 Japan King Mongkuts Univ Technol Thonburi Sch Informat & Technol 126 Pracha Uthit Rd Bangkok 10140 Thailand

Since the beginning of the smartphone era, the number of smartphone users has increased dramatically. Consequently, several aspects of their use have provoked concerns from researchers. From a health-related perspective, text neck syndrome is a consequence of excessive smartphone use. Therefore, we introduce a neck posture monitoring system to help prevent text neck syndrome in smartphone users. The system operates on smartphones using the embedded rotation vector sensors and the camera for image detection. Data from the sensors and camera are used to calculate the neck angle. The results of an experiment show that the calculation algorithm is not significantly different from the photogrammetric method, which was proposed earlier as a method to measure the neck angle from a side view image. In summary, we propose a system that includes the detection and classification of the user's neck posture to raise awareness and help promote better posture to prevent text neck syndrome among smartphone users. (c) 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： text neck smartphone usage posture monitoring image detection classification

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Bayesian-Convolutional Neural Network Model Transfer Learning for image detection of Concrete Water-Binder Ratio

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IEEE ACCESS 2020年 8卷 35350-35367页

作者： Yang, Han Jiao, Shuangjian Sun, Peng Ocean Univ China Coll Engn Dept Civil Engn Qingdao 266100 Shandong Peoples R China

Since concrete is still the most widely used building material at present, the phenomenon of engineering accidents caused by inaccurate mix proportion is extremely prominent. This makes the contradiction between timely and effective detection and test results cannot be given quickly by traditional technology become particularly outstanding. In this paper, a new method based on Bayesian-convolutional neural network model transfer learning is proposed to detect water-binder ratio, the most important parameter in mix proportion of concrete mixtures. Bayesian optimization was applied to pretrained convolutional neural networks to establish Bayesian-convolutional neural network models, avoiding tuning hyperparameters manually. The authors performed several experiments and obtained large numbers of images of freshly-mixed concrete mixtures, which were used as datasets to carry out water-binder ratio detection. These models achieved high accuracies on training, validation and testing sets. Applying these models, we could implement real-time and high sampling rate water-binder ratio detection. The authors integrated the models and developed a detection system of concrete mix proportion. Equipped with definite hardware facilities, this system can effectively monitor the quality of concrete in production process and prevent engineering accidents. According to training curves of the models, a new parameter was introduced to discuss how mix proportion influence the sensitivity of concrete mixtures apparent state to the change of water-binder ratio, which is an important consideration to preliminary assess the service behaviors of concrete. Through this parameter, we also explored the essence of image features learned by models is the fluidity of concrete mixtures.

关键词： Bayesian-convolutional neural network model computer aided engineering image detection mix proportion transfer learning water-binder ratio

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