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Design of a real-time crime monitoring system using deep learning techniques

INTELLIGENT SYSTEMS WITH APPLICATIONS 2024年 21卷

作者： Mukto, Md. Muktadir Hasan, Mahamudul Al Mahmud, Md. Maiyaz Haque, Ikramul Ahmed, Md. Ahsan Jabid, Taskeed Ali, Md. Sawkat Rashid, Mohammad Rifat Ahmmad Islam, Mohammad Manzurul Islam, Maheen East West Univ Dept Comp Sci & Engn Dhaka 1212 Bangladesh

Criminal Activities and Crime Monitoring System has long been a research topic. In this paper, we propose an effective Crime Monitoring System (CMS) that can detect a crime in real-time using a camera surveillance system and notify the appropriate law enforcement officer. The CMS was proposed to counterbalance human weaknesses such as inattention, slow reaction, and slacking, for example, in detecting crimes. The proposed CMS detects crime scenes by combining the mechanisms and functionalities of closed-circuit television (CCTV) cameras with various deep-learning methods and image-processing techniques. The CMS operates in three stages: weapon detection, violence detection, and face recognition. We used transfer learning models to detect weapons, and violence and used a face recognition algorithm to recognize faces. More specifically, to detect weapons YOLOv5, and MobileNetv2 to detect violence and used face recognition algorithms to recognize faces. The image dataset is used in CMS, while the video dataset is used to train the MobileNet-based violence detection model. In this case, frame-by-frame images extracted from video files were used to train the model. All of the models performed admirably. The weapon detection model detected four different weapon classes with greater than 80% accuracy. The violence detection model is also 95% accurate. The face recognition model had a 97% accuracy rate in detecting faces. The CMS's combined model was tested in a variety of real-world scenarios, and its performance was found to be outstanding. It was able to detect crime incidents and generate timely alarms, demonstrating its effectiveness in providing security and safety.

关键词： Crime monitoring system (CMS) Surveillance Weapon detection Violence detection Face recognition deep learning image processing

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deep learning using a residual deconvolutional network enables real-time high-density single-molecule localization microscopy

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BIOMEDICAL OPTICS EXPRESS 2023年第4期14卷 1833-1847页

作者： Zhou, Zhiwei Wu, Junnan Wang, Zhengxia Huang, Zhen-li Huazhong Univ Sci & Technol Britton Chance Ctr Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Engn Sci MoE Key Lab Biomed Photon Wuhan Natl Lab Optoelect Wuhan 430074 Peoples R China Hainan Univ Sch Biomed Engn Key Lab Biomed Engn Hainan Prov Haikou 570228 Peoples R China Hainan Univ Sch Comp Sci & Technol Haikou 570228 Peoples R China

High-density localization based on deep learning is a very effective method to accelerate single molecule localization microscopy (SMLM). Compared with traditional highdensity localization methods, deep learning-based methods enable a faster data processing speed and a higher localization accuracy. However, the reported high-density localization methods based on deep learning are still not fast enough to enable real time data processing for large batches of raw images, which is probably dueto the heavy computational burden and computation complexity in the U-shape architecture used in these models. Here we propose a high-density localization method called FID-STORM, which is based on an improved residual deconvolutional network for the real-time processing of raw images. In FID-STORM, we use a residual network to extract the features directly from low-resolution raw images rather than the U-shape network from interpolated images. We also use a model fusion from TensorRT to further accelerate the inference of the model. In addition, we process the sum of the localization images directly on GPU to obtain an additional speed gain. Using simulated and experimental data, we verified that the FID-STORM method achieves a processing speed of 7.31 ms/frame at 256 x 256 pixels @ Nvidia RTX 2080 Ti graphic card, which is shorter than the typical exposure time of 10-30 ms, thus enabling real-time data processing in high-density SMLM. Moreover, compared with a popular interpolated image-based method called deep-STORM, FID-STORM enables a speed gain of -26 times, without loss of reconstruction accuracy. We also provided an imageJ plugin for our new method.

关键词： deep learning Imaging techniques Neural networks Spatial resolution Super resolution imaging Wavefront distortions

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real-time tunnel lining leakage image semantic segmentation via multiple attention mechanisms

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第7期35卷 075204-075204页

作者： Tan, Yonghui Li, Xiaolong Lai, Jinfu Ai, Jinquan East China Univ Technol Key Lab Mine Environm Monitoring & Improving Poyan Minist Nat Resources Nanchang 330013 Jiangxi Peoples R China East China Univ Technol CNNC Engn Res Ctr 3D Geog Informat Nanchang 330013 Jiangxi Peoples R China Three Gorges Geotech Engn Co Ltd Wuhan 430074 Hubei Peoples R China

One of the key objectives in tunnel illness detection is identifying tunnel lining leakage, and deep learning-based image semantic segmentation approaches can automatically locate tunnel lining leakage. However, in order to meet the real-time processing needs of professional mobile inspection equipment, existing leakage image segmentation approaches have difficulties in identifying real-time, dealing with voids, and dealing with edge discontinuities in the leaking zone. To address the aforementioned issues, this study introduces the PP-LiteSeg-Attn model, which takes the real-time semantic segmentation model PP-LiteSeg-B as baseline model, and combines the multi-layer CBAM attention mechanism and the CoT attention mechanism. Using the publically available dataset Water-Leakage, we trained and validated the PP-LiteSeg-Attn model, and attained IoU and F1 values of 88.18% and 93.72%, respectively, outperforming similar models in both measures. Extensive experiments show that the segmentation speed of the PP-LiteSeg-Attn model reaches 112.28 FPS, which meets real-time requirements, and that the model can effectively solve problems such as the appearance of voids in the seepage area, discontinuity, and fuzzy segmentation of seepage edges. The PP-LiteSeg-Attn model is better applicable to complicated tunnel settings, offering technical references for real-time diagnosis of tunnel illnesses.

关键词： deep learning tunnel engineering semantic segmentation water leakage attention mechanism PP-LiteSeg

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DroMOD: A Drone-Based Multi-Scope Object Detection System

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IEEE ACCESS 2023年 11卷 26652-26666页

作者： Abdellatif, Takoua Sedrine, Mohamed Ali Gacha, Yassine Univ Carthage SERCOM Lab Carthage 1054 Tunisia Univ Sousse ENISo Sousse 4002 Tunisia Aviat Sch Borj El Amri SACES Borj El Amri 1142 Tunisia Natl Ctr Mapping & Remote Sensing CNCT El Aouina 1080 Tunisia

Drone-based Multi-scope Object Detection (DroMOD) system targets an efficient detection of different kinds of objects using drones. DroMOD relies on a cross-platform framework where objects' detection tasks are shared between the drone and the server. The drone stores a set of reference images about the supervised zone. Each time an image is captured with a well-defined spatial frequency, it is compared to its reference image and only 'trigger images' showing a change from the reference image are sent to the server. A Big Data streaming platform is deployed on server-side for scalable and efficient real-time object detection processing based on deep learning (DL) models. DroMOD system architecture allows for dynamically upgrading the DL models so that newly considered objects to detect can be added to the drone mission on the fly without modifying the drone embedded software. When compared to existing alternatives, DroMOD presents the best compromise between 1) object detection accuracy, 2) real-time processing, and 3) resource efficiency. Since only lightweight processing is performed on the drone-side, memory and computation are highly optimised on drones. Furthermore, the drone-side image filtering is independent of the objects to detect and the object detection programs are deployed and updated only on the server-side which allows for multi-target detection with minimal engineering efforts and expertise.

关键词： Drones Object detection real-time systems Servers Data models Streaming media Visualization Big data deep learning drone and server-side processing collaboration image comparison key frame selection multi-scope object detection

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A Comprehensive Review on image Encryption Techniques using Memristor based Chaotic System for Multimedia Application

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IETE JOURNAL OF RESEARCH 2024年第11期70卷 8160-8183页

作者： Kumar, B. Sakthi Revathi, R. Sri Eshwar Coll Engn Dept ECE Coimbatore India KLEF Univ Dept ECE Guntur India

image encryption is one of the most widely used techniques for securing images in trusted and unrestricted public media. However, the drawback is weak security in chaotic encryption algorithms, small key space and hardware implementation complexities. Various appropriate encryption algorithms have been carried out for secure image transmission;it becomes a critical issue to protect image integrity, confidentiality and authenticity. To overcome these issues, this article examines several existing memristor-based image encryption algorithms with various chaotic maps. As a result, this research aims to deliver comprehensive literature on image encryption techniques through memristor to help the researchers. Finally, this survey provides all vital literature on the current image encryption techniques with their benefits, drawbacks, developments, and future directions. We also give a general guideline about cryptography. We conclude that all methods are helpful for real-time image encryption. A comparison has been made between many image processing approaches (conventional and memristor) based on metrics such as the number of pixels change rate (NPCR) with the value of 0.9986, histogram, entropy (7.9993), unified average varying intensity (UACI) achieves the value as 0.4996, energy consumption as 0.32pJ and time complexity as 0.9s. The experimental results of various approaches, key sensitivity and statistical analysis revealed that the survey of memristor-based image encryption schemes provides an effective way to secure image transmission in real-time application.

关键词： deep learning image decryption image encryption image processing Memristor Security

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An effective IoT interface considering an eye-tracking method for autonomous vehicle

INTERNET OF THINGS

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INTERNET OF THINGS 2025年 31卷

作者： Park, Junghoon Ajou Univ Coll Comp & Informat Dept Appl Artificial Intelligence WorldCup Ro 206 Suwon 16499 Gyeonggi Do South Korea

The number of Internet-connected devices is steadily increasing, exceeding 25 billion globally and projected to surpass 50 billion about 6.5 times the world population. At the core of IoT is data collection via sensors, forming big data for AI-driven analysis, optimization, and visualization. A convenient control environment is essential for devices like autonomous vehicles, requiring new interfaces such as touchless eye movement. This research proposes a real-time eye-tracking method using a single web camera, easily installed in cars and integrated with IoT. The system detects gaze by recognizing iris shape, enabling software-only tracking without extra hardware. Experiments show a mean absolute error (MAE) of 3.49 degrees, ensuring accuracy even with head movement. Unlike existing infrared (IR) LED or head-mounted methods, this approach offers a cost-effective, real-time solution. Using lightweight image processing instead of deep learning, the system achieves real-time tracking with low latency, making it ideal for low-power IoT and autonomous vehicles. It is expected to become a next-generation input interface for these applications.

关键词： Autonomous system Eye gaze tracking IoT image processing Interpolation real-time processing

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Space Imaging Point Source Detection and Characterization

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IEEE ACCESS 2024年 12卷 90442-90460页

作者： Ribeiro, Francisco S. F. Garcia, Paulo J. V. Silva, Miguel Cardoso, Jaime S. Univ Porto Fac Engn P-4250465 Porto Portugal Inst Super Tecn CENTRA P-1049001 Lisbon Portugal INESC TEC P-4200465 Porto Portugal

Point source detection algorithms play a pivotal role across diverse applications, influencing fields such as astronomy, biomedical imaging, environmental monitoring, and beyond. This article reviews the algorithms used for space imaging applications from ground and space telescopes. The main difficulties in detection arise from the incomplete knowledge of the impulse function of the imaging system, which depends on the aperture, atmospheric turbulence (for ground-based telescopes), and other factors, some of which are time-dependent. Incomplete knowledge of the impulse function decreases the effectiveness of the algorithms. In recent years, deep learning techniques have been employed to mitigate this problem and have the potential to outperform more traditional approaches. The success of deep learning techniques in object detection has been observed in many fields, and recent developments can further improve the accuracy. However, deep learning methods are still in the early stages of adoption and are used less frequently than traditional approaches. In this review, we discuss the main challenges of point source detection, as well as the latest developments, covering both traditional and current deep learning methods. In addition, we present a comparison between the two approaches to better demonstrate the advantages of each methodology.

关键词： Noise measurement Detectors deep learning Apertures Signal to noise ratio Atmospheric measurements image processing Machine learning Object detection image annotation digital images high-resolution imaging machine learning object detection

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An Enhanced Lung Cancer Detection Approach Using Dual-Model deep learning Technique

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 835-867页

作者： Sumaia Mohamed Elhassan Saad Mohamed Darwish Saleh Mesbah Elkaffas Department of Information Technology Institute of Graduate Studies and ResearchAlexandria UniversityAlexandria21526Egypt College of Computing and Information Technology Arab Academy for ScienceTechnology and Maritime TransportAlexandriaP.O.Box 1029Egypt

Lung cancer continues to be a leading cause of cancer-related deaths worldwide,emphasizing the critical need for improved diagnostic *** detection of lung tumors significantly increases the chances of successful treatment and ***,current diagnostic methods often fail to detect tumors at an early stage or to accurately pinpoint their location within the lung ***-model deep learning technologies for lung cancer detection,while beneficial,cannot capture the full range of features present in medical imaging data,leading to incomplete or inaccurate ***,it may not be robust enough to handle the wide variability in medical images due to different imaging conditions,patient anatomy,and tumor *** overcome these disadvantages,dual-model or multi-model approaches can be *** research focuses on enhancing the detection of lung cancer by utilizing a combination of two learning models:a Convolutional Neural Network(CNN)for categorization and the You Only Look Once(YOLOv8)architecture for real-time identification and pinpointing of *** automatically learn to extract hierarchical features from raw image data,capturing patterns such as edges,textures,and complex structures that are crucial for identifying lung ***8 incorporates multiscale feature extraction,enabling the detection of tumors of varying sizes and scales within a single *** is particularly beneficial for identifying small or irregularly shaped tumors that may be challenging to ***,through the utilization of cutting-edge data augmentation methods,such as deep Convolutional Generative Adversarial Networks(DCGAN),the suggested approach can handle the issue of limited data and boost the models’ability to learn from diverse and comprehensive *** combined method not only improved accuracy and localization but also ensured efficient real-time processing,which is crucial for practical clinical applicati

关键词： Lung cancer detection dual-model deep learning technique data augmentation CNN YOLOv8

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Recent Advances in deep-learning-Based SAR image Target Detection and Recognition

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 6884-6915页

作者： Lang, Ping Fu, Xiongjun Dong, Jian Yang, Huizhang Yin, Junjun Yang, Jian Martorella, Marco Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Beijing Inst Technol Sch Integrated Circuits & Elect Beijing 100081 Peoples R China Nanjing Univ Sci & Technol Sch Elect & Opt Engn Nanjing 210094 Peoples R China Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China Univ Pisa Dept Informat Engn I-56127 Pisa Italy

Synthetic aperture radar (SAR) image target detection and recognition (SAR-TDR) tasks have become research hot spots in the remote sensing application. These targets include ships, vehicles, aircraft, oil tanks, bridges, and so on. However, with the rapid development of SAR technology and increasingly complex electromagnetic environment, complex characteristics of SAR images bring severe challenges to the accurate SAR-TDR via traditional physical models or manual feature-extraction-based machine learning methods. In recent years, deep learning (DL), as a powerful automatic feature extraction algorithm, has been widely used in the computer vision domain. More specifically, DL has also been introduced into the SAR-TDR tasks and has effectively achieved good performance in terms of accuracy, real-time processing, etc. With the rapid development of DL, SAR image processing, and practical requirements of SAR-TDR in civilian and military domains, it is crucial to conduct a systematic survey on SAR-TDR in the past few years. In this survey article, we mainly conduct a systematic overview of DL-based SAR-TDR literature on two tasks, i.e., target recognition (e.g., ground vehicles, ships, and aircraft) and target detection (e.g., ships, aircraft, change detection, sea surface oil spills, and oil tanks). More specifically, our related works about these topics are also presented to verify the effectiveness of DL-based methods. First, several DL methods (e.g., convolutional neural networks, recurrent neural networks, autoencoders, and generative adversarial networks), commonly used in SAR-TDR, are briefly introduced. Then, a systematic review of DL-based SAR-TDR (including our related works) is presented. Finally, the current challenges and future possible research directions are deeply analyzed and discussed.

关键词： Radar polarimetry Target recognition Synthetic aperture radar Marine vehicles Feature extraction Object detection Aircraft Surveys Accuracy deep learning Automatic target recognition change detection (CD) deep learning (DL) SAR image interpretation target detection

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Lightweight deep learning-based automated concrete surface inspection: Crack classification and pixel-level segmentation

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SMART STRUCTURES AND SYSTEMS 2025年第3期35卷 163-175页

作者： Yang, Dong Cai, Yuan-Yuan Xu, En-Dian Zhang, Jing Yuan, Ye Wang, Yan-Jia Guangzhou Univ Earthquake Engn Res & Test Ctr EERTC Guangzhou Peoples R China Guangzhou Univ Key Lab Earthquake Resistance Earthquake Mitigat & Struct Safety Minist Educ Guangzhou Peoples R China Hefei Univ Technol Dept Civil Engn Hefei Anhui Peoples R China Jinan Univ Dept Mech & Construct Engn Guangzhou Peoples R China Univ Hong Kong Dept Civil Engn Pokfulam Rd Hong Kong Peoples R China

Crack detection is an important measure in the field of structural health monitoring. However, visual crack detection is labor-intensive, time-consuming, inefficient, and expensive. Although image-based detection and processing provides an efficient way for structural crack detection, its accuracy depends on image quality. For engineering structures, especially bridges, the change of light conditions and the difference of surface characteristics of structural components pose a major challenge to traditional crack detection methods. In this paper, a novel crack detection method based on convolutional neural networks is proposed. The development of this method is divided into the following stages. The initial automated crack classification is carried out by using MobileNetV3, and then the improved deepLabv3+ network is used to segment the classified crack image semantically accurately. Finally, the real crack image is used for verification. To verify the proposed method, several conventional deep learning networks are trained and compared. The improved deepLabV3+ integrates MobileNetV3 as its feature extraction backbone and incorporates the convolutional block attention module, which achieves 87.79% average intersection and 93.87% average pixel accuracy on public and real data sets. Compared with traditional models such as VGG16, the proposed method shortens the training time by more than 80% while maintaining high detection accuracy. In addition, the compact parameter configuration and moderate model size make it particularly suitable for deployment on mobile detection devices.

关键词： attention mechanism crack detection improved deepLabv3+ lightweight deep learning semantic segmentation

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