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2024 International Conference on Virtual reality, image and Signal processing, ICVISP 2024

作者： Chen, Lu Xia, Deping Liu, Deshun Nanjing Research Institute of Electronic Technology Nanjing China

ISBN: (纸本)9798400710926

To address issues such as mainlobe distortion and sidelobe elevation in airborne monostatic radar for mainlobe jamming suppression, this paper proposes a mainlobe jamming suppression algorithm for airborne bistatic radar based on deep neural networks. This method leverages the real-time computational capabilities and powerful learning abilities of deep neural networks, using unsupervised learning to dynamically compute weights. These weights are then multiplied with the received signals from the auxiliary station to accurately estimate the jamming signal at the main station, achieving mainlobe jamming cancellation for both the primary and auxiliary radars. Simulation results show that using deep neural networks to calculate weight vectors for mainlobe jamming cancellation not only effectively suppresses mainlobe jamming but also reduces the signal-to-noise ratio loss of the target to about 3dB, significantly enhancing the system's real-time performance and anti-jamming capabilities. © 2024 Copyright held by the owner/author(s).

关键词： Signal to noise ratio

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End-to-End learning-Based image Compression With a Decoupled Framework

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第5期34卷 3067-3081页

作者： Zhang, Zhaobin Esenlik, Semih Wu, Yaojun Wang, Meng Zhang, Kai Zhang, Li Bytedance Inc Adv Video Grp AVG San Diego CA 92122 USA

The autoregressive model has been widely used in learning-based image compression due to its superior context modeling capability. However, its sequential processing nature also undermines the ability of decoding in parallel and hinders the deployment in real applications. In this paper, we propose a decoupled framework to resolve this issue. With the decoupled architecture, the entropy decoding process is independent of the latent sample reconstruction process. The entropy decoding process thus can be finished before the latent sample prediction process begins, which leads to significant decoding time savings by enabling the two processes to be conducted in parallel. To further reduce the decoding time, we introduce wavefront processing, where multiple rows can be processed simultaneously when reconstructing the latent samples. On top of that, we design a series of coding tools to improve the rate-distortion efficiency and reduce the decoding complexity. Device interoperability is also supported by the proposed solution, where the same bitstream can be successfully decoded on different CPU/GPU devices. Comprehensive experiments are conducted to validate the effectiveness of the proposed method. Using objective evaluation metrics required by JPEG AI Call for Proposals (CfP), the proposed method achieves a BD-rate change of -29.6% on average with 2.44 times faster decoding speed compared to VVC image coding. When compared to the commonly used benchmark learning-based methods, the proposed method achieves -30.5% BD-rate changes and 101 times faster decoding speed over cheng2020attn. The proposed solution has been proposed to JPEG AI and IEEE 1857.11 as a response to CfP and the core techniques have been adopted by both.

关键词： Decoding image coding Entropy image reconstruction Arithmetic Standards Transforms Decoupled end-to-end IEEE 1857.11 image compression JPEG AI neural network wavefront

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Research on improvement of defect detection system for printed circuit board based on deep learning

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2025年第3期25卷 2253-2262页

作者： Li, Cuihua Xue, Cuiwei Jia, Guiquan Zhang, Hui Jiang, Guoyi Hefei Univ Sch Adv Mfg Engn Hefei Peoples R China

As the core component of electronic products, the quality of PCB (print circuit board) directly affects the performance and reliability of the entire product. The traditional PCB defect detection methods mainly rely on manual visual inspection and image processing technology, which have problems such as low efficiency and easy errors and cannot meet the needs of large-scale production. The PCB defect detection method based on deep learning and object detection technology has gradually become mainstream. This article focuses on the research of PCB defect object detection method based on deep learning. Therefore, the paper conducts relevant intensive study on PCB defect detection, aiming to develop a model that is accurate, compact, and suitable for real-time detection. Specific improvements include using C2F_The ViT module replaces the original C2F, and the purpose is to facilitate the model to comprehensively consider the local details of PCB defects and the global contextual semantic information. Introduces multi-head self-attention mechanism (MHSA), adds detection heads in the p2 layer, and improves defect detection accuracy and location accuracy. Extract the target semantic information of small defects and suppress the local background information at the same time. The experimental results indicate that the detection algorithm can improve detection accuracy. Compared with the original YOLOv8, the improved model has a 4.9% and 7.9% improvement in map50 and map50-95, respectively.

关键词： PCB defect detection deep machine learning convolutional neural network improved YOLOv8

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Fast detection of bag-breakups in pulsating and steady airflow using video analysis and deep learning

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JOURNAL OF real-time image processing 2023年第6期20卷 114页

作者： Morita, Daiki Raytchev, Bisser Elhanashi, Abdussalam Kawaguchi, Mikimasa Ogata, Yoichi Higaki, Toru Kaneda, Kazufumi Nakashima, Akira Saponara, Sergio Hiroshima Univ Grad Sch Adv Sci & Engn Higashihiroshima Japan Univ Pisa Dip Ingn Informaz Pisa Italy Mazda Motor Corp MBD Innovat Dept Fuchu Japan

Object detection methods based on deep learning have made great progress in recent years and have been used successfully in many different applications. However, since they have been evaluated predominantly on datasets of natural images, it is still unclear how accurate and effective they can be if used in special domain applications, for example in scientific, industrial, etc. images, where the properties of the images are very different from those taken in natural scenes. In this study, we illustrate the challenges one needs to face in such a setting on a concrete practical application, involving the detection of a particular fluid phenomenon-bag-breakup-in images of droplet scattering, which differ significantly from natural images. Using two technologically mature and state-of-the-art object detection methods, RetinaNet and YOLOv7, we discuss what strategies need to be considered in this problem setting, and perform both quantitative and qualitative evaluations to study their effects. Additionally, we also propose a new method to further improve accuracy of detection by utilizing information from several consecutive frames. We hope that the practical insights gained in this study can be of use to other researchers and practitioners when targeting applications where the images differ greatly from natural images.

关键词： Object detection Scientific and industrial applications real-time processing Small-size datasets YOLOv7 RetinaNet

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Industrial object detection with multi-modal SSD: closing the gap between synthetic and real images

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MULtimeDIA TOOLS AND APPLICATIONS 2024年第4期83卷 12111-12138页

作者： Cohen, Julia Crispim-Junior, Carlos Chiappa, Jean-Marc Rodet, Laure Tougne Univ Lyon Univ Lyon 2 CNRS Cent LyonINSA LyonUCBLLIRISUMR 5205 F-69676 Bron France DEMS St Bonnet de Mure France

Object detection for industrial applications faces challenges that are yet to solve by state-of-the-art deep learning models. They usually lack training data, and the common solution of using a synthetic dataset introduces a domain gap when the model is provided real images. Besides, few architectures fit in the small memory of a mobile device and run in real-time with limited computation capabilities. The models fulfilling these requirements generally have low learning capacity, and the domain gap reduces further the performance. In this work, we propose multiple strategies to reduce the domain gap when using RGB-D images, and to increase the overall performance of a Convolutional Neural Network (CNN) for object detection with a reasonable increase of the model size. First, we propose a new architecture based on the Single Shot Detector (SSD) architecture, and we compare different fusion methods to increase the performance with few or no additional parameters. We applied the proposed method to three synthetic datasets with different visual characteristics, and we show that classical image processing reduces significantly the domain gap for depth maps. Our experiments have shown an improvement when fusing RGB and depth images for two benchmark datasets, even when the depth maps contain few discriminative information. Our RGB-D SSD Lite model performs on par or better than a ResNet-FPN RetinaNet model on the LINEMOD and T-LESS datasets, while requiring 20 times less computation. Finally, we provide some insights on training a robust model for improved performance when one of the modalities is missing.

关键词： Object detection deep learning Synthetic dataset Industrial RGB-D

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Timing Performance Benchmarking of Out-of-Distribution Detection Algorithms

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JOURNAL OF SIGNAL processing SYSTEMS FOR SIGNAL image AND VIDEO TECHNOLOGY 2023年第12期95卷 1355-1370页

作者： Luan, Siyu Gu, Zonghua Saremi, Amin Freidovich, Leonid Jiang, Lili Wan, Shaohua Umea Univ Dept Appl Phys & Elect Umea Sweden Umea Univ Dept Comp Sci Umea Sweden Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen Peoples R China

In an open world with a long-tail distribution of input samples, deep Neural Networks (DNNs) may make unpredictable mistakes for Out-of-Distribution (OOD) inputs at test time, despite high levels of accuracy obtained during model training. OOD detection can be an effective runtime assurance mechanism for safe deployment of machine learning algorithms in safety-critical applications such as medical imaging and autonomous driving. A large number of OOD detection algorithms have been proposed in recent years, with a wide range of performance metrics in terms of accuracy and execution time. For real-time safety-critical applications, e.g., autonomous driving, timing performance is of great importance in addition to accuracy. We perform a comprehensive and systematic benchmark study of multiple OOD detection algorithms in terms of both accuracy and execution time on different hardware platforms, including a powerful workstation and a resource-constrained embedded device, equipped with both CPU and GPU. We also profile and analyze the internal details of each algorithm to identify the performance bottlenecks and potential for GPU acceleration. This paper aims to provide a useful reference for the practical deployment of OOD detection algorithms for real-time safety-critical applications.

关键词： Machine learning deep learning Out-of-Distribution detection real-time systems Embedded systems

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Diffraction denoising using self-supervised learning

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GEOPHYSICAL PROSPECTING 2023年第7期71卷 1215-1225页

作者： Markovic, Magdalena Malehmir, Reza Malehmir, Alireza Uppsala Univ Dept Earth Sci Uppsala Sweden Tetra Tech Vancouver BC Canada Uppsala Univ Dept Earth Sci SE-75236 Uppsala Sweden

Diffraction wavefield contains valuable information on subsurface composition through velocity extraction and sometimes anisotropy estimation. It can also be used for the delineation of geological features, such as faults, fractures and mineral deposits. Diffraction recognition is, therefore, crucial for improved interpretation of seismic data. To date, many workflows for diffraction denoising, including deep-learning applications, have been provided, however, with a major focus on sedimentary settings or for ground-penetrating radar data. In this study, we have developed a workflow for a self-supervised learning technique, an autoencoder, for diffraction denoising on synthetic seismic, ground-penetrating radar and hardrock seismic datasets. The autoencoder provides promising results especially for the ground-penetrating radar data. Depending on the target of the studies, diffraction signals can be tackled using the autoencoder both as the signal and/or noise when, for example, a reflection is a target. The real hardrock seismic data required additional pre- and post-autoencoder image processing steps to improve automatic delineation of the diffraction. Here, we also coupled the autoencoder with Hough transform and pixel edge detection filters. Along inlines and crosslines, diffraction signals have sometimes a similar character as the reflection and may spatially be correlated making the denoising workflow unsuccessful. Coupled with additional image processing steps, we successfully isolated diffraction that is generated from a known volcanogenic massive sulphide deposit. These encouraging results suggest that the self-supervised learning techniques such as the autoencoder can be used also for seismic mineral exploration purposes and are worthy to be implemented as additional tools for data processing and target detections.

关键词： autoencoder deep learning diffraction ground-penetrating radar mineral exploration self-supervised

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deep learning-Based Single-image Super-Resolution: A Comprehensive Review

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IEEE ACCESS 2023年 11卷 21811-21830页

作者： Chauhan, Karansingh Patel, Shail Nimish Kumhar, Malaram Bhatia, Jitendra Tanwar, Sudeep Davidson, Innocent Ewean Mazibuko, Thokozile F. F. Sharma, Ravi Dalhousie Univ Dept Comp Sci Halifax NS B3H 4R2 Canada Ahmedabad Univ Dept Comp Sci & Engn Ahmadabad 380009 Gujarat India Nirma Univ Inst Technol Dept Comp Sci & Engn Ahmadabad 382481 Gujarat India Cape Peninsula Univ Technol Dept Elect Elect & Comp Engn ZA-7535 Bellville South Africa Durban Univ Technol Dept Elect Power Engn ZA-4000 Durban South Africa Univ Petr & Energy Studies Ctr Interdisciplinary Res & Innovat Dehra Dun 248001 India

High-fidelity information, such as 4K quality videos and photographs, is increasing as high-speed internet access becomes more widespread and less expensive. Even though camera sensors' performance is constantly improving, artificially enhanced photos and videos created by intelligent image processing algorithms have significantly improved image fidelity in recent years. Single image super-resolution is a class of algorithms that produces a high-resolution image from a given low-resolution image. Since the advent of deep learning a decade ago, this field has made significant strides. This paper presents a comprehensive review of the deep learning assisted single image super-resolution domain including generative adversarial network (GAN) models that discusses the prominent architectures, models used, and their merits and demerits. The reason behind covering the GAN models is that it is been known to perform better than the conventional deep learning methods given the resources and the time. For real-world applications with noise and other issues that can cause low-fidelity super resolution (SR) images, we examine another solution based on GAN model. This GAN model-based technique popularly known as blind super resolution is more resilient. We examined the various super-resolution techniques by varying image scaling factors (i.e., 2x, 3x, 4x) to measure PSNR and SSIM metrics for the different datasets. PSNR across the different datasets covered in the experimental Section shows an average of 14-17 % decrease in the score as we move up the image resolution scale from 2x to 4x. This is observed across all the datasets and for every model mentioned in the experimental Section of the paper. The results also show that blind super-resolution outperforms the conventional deep learning methods and the more complex GAN models. GAN models are complex and preferred when the upscale factor is high, while residual and dense models are recommended for smaller upscaling factors. Th

关键词： Superresolution deep learning Generative adversarial networks Interpolation Mathematical models Videos Feature extraction image super-resolution deep learning convolutional neural network generative adversarial network

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Towards complex scenes: A deep learning-based camouflaged people detection method for snapshot multispectral images

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Defence Technology（防务技术） 2024年第4期34卷 269-281页

作者： Shu Wang Dawei Zeng Yixuan Xu Gonghan Yang Feng Huang Liqiong Chen School of Mechanical Engineering and Automation Fuzhou UniversityFuzhou 350108China

Camouflaged people are extremely expert in actively concealing themselves by effectively utilizing cover and the surrounding environment. Despite advancements in optical detection capabilities through imaging systems, including spectral, polarization, and infrared technologies, there is still a lack of effective real-time method for accurately detecting small-size and high-efficient camouflaged people in complex real-world scenes. Here, this study proposes a snapshot multispectral image-based camouflaged detection model, multispectral YOLO(MS-YOLO), which utilizes the SPD-Conv and Sim AM modules to effectively represent targets and suppress background interference by exploiting the spatial-spectral target information. Besides, the study constructs the first real-shot multispectral camouflaged people dataset(MSCPD), which encompasses diverse scenes, target scales, and attitudes. To minimize information redundancy, MS-YOLO selects an optimal subset of 12 bands with strong feature representation and minimal inter-band correlation as input. Through experiments on the MSCPD, MS-YOLO achieves a mean Average Precision of 94.31% and real-time detection at 65 frames per second, which confirms the effectiveness and efficiency of our method in detecting camouflaged people in various typical desert and forest scenes. Our approach offers valuable support to improve the perception capabilities of unmanned aerial vehicles in detecting enemy forces and rescuing personnel in battlefield.

关键词： Camouflaged people detection Snapshot multispectral imaging Optimal band selection MS-YOLO Complex remote sensing scenes

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Enhancing Safety and Workover Efficiency Through real- time Scale-Adaptive Video Detection of Critical Objects at the Wellsite

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SPE JOURNAL 2024年第8期29卷 3982-3999页

作者： Zhang, Kai Song, Zewen Xia, Xiaolong Zhang, Liming Yang, Yongfei Sun, Hai Yao, Jun Zhang, Huaqing Zhang, Yue Feng, Gaocheng Liu, Chen China Univ Petr East China Qingdao Peoples R China Qingdao Univ Technol Qingdao Peoples R China CNOOC EnerTech Drilling & Prod Co Tianjin Peoples R China CNOOC Res Inst Ltd Beijing Peoples R China

The wellsite serves as the fundamental unit in the development of oil and gas fields, functioning as a hub for the production activities, with workover operations being a critical means to ensure production continuity. In addition, it plays a crucial role in environmental protection, preventing oil and gas leakage and pollution. Various pieces of mechanical equipment deployed at the wellsite are essential for tasks such as oil and gas extraction and well repair operations, holding a pivotal position in oil- and gasfield development. Consequently, intelligent wellsite implementation necessitates a primary focus on monitoring mechanical equipment, with video emerging as a vital form of multisource information at the wellsite. While existing research on wellsite video monitoring predominantly addresses system and data transmission issues, it falls short in addressing the challenges of real- time assessment and early warning in intelligent wellsite operations. This study introduces a method for identifying critical targets at the wellsite based on a scale- adaptive network. The model employs a multiscale fusion network to extract different image features and semantic features at various scales, facilitating their fusion. The processing of wellsite video images occurs in multiple stages, outputting predicted box locations and category information, enabling the localization and recognition of critical objects at the wellsite. Unlike traditional deep convolutional object detection methods, this model incorporates a parameter- free attention mechanism, enhancing the accurate feature learning of small targets during the extraction process and addressing the issue of multiscale imbalance. The experimental results validate the robust performance of the method, surpassing the latest one- stage object detection models and mainstream loss function methods. Comparative experiments demonstrate a 9.22% improvement in mean average precision (mAP) compared with YOLOv8, establishing th

关键词： deep learning artificial intelligence detection wellsite machine learning operation information neural network real time system algorithm

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