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A one-stage deep learning based method for automatic analysis of droplet-based digital PCR images

ANALYST 2023年第13期148卷 3065-3073页

作者： Yao, Yuanyang Zhao, Shuhao Liang, Yan Hu, Fei Peng, Niancai Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710054 Shaanxi Peoples R China

Droplet-based dPCR offers many advantages over chip-based dPCR, such as lower processing cost, higher droplet density, higher throughput, while requiring less sample. However, the stochastic nature of droplet locations, uneven illuminations, and unclear droplet boundaries make automatic image analysis challenging. Most methods currently used to count a large amount of microdroplets rely on flow detection. Conventional machine vision algorithms cannot extract all information of the targets from complex backgrounds. Some two-stage methods, which first locate and then classify droplets according to their grayscale values, require high-quality imaging. In this study, we addressed these limitations by improving a one-stage deep learning algorithm named YOLOv5 and applying it to the detection task to realize one-stage detection. We introduced an attention mechanism module to increase the detection rate of small targets and used a new loss function to speed up the training process. Furthermore, we employed a network pruning method to facilitate the deployment of the model on mobile devices while preserving its performance. We validated the model with captured droplet-based dPCR images and found that the improved model accurately identified negative and positive droplets in complex backgrounds with an error rate of 0.65%. This method is characterized by its fast detection speed, high accuracy, and ability to be used on mobile devices or cloud platforms. Overall, the study presents a novel approach for detecting droplets in large-scale microdroplet images and provides a promising solution for accurate and efficient droplet counting in droplet-based dPCR.

关键词： Stochastic systems

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Non Homogeneous Realistic Single image Dehazing

Non Homogeneous Realistic Single Image Dehazing

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23rd IEEE/CVF Winter conference on Applications of Computer Vision (WACV)

作者： Vinay, P. Abhisheka, K. S. Shetty, Lithesh Kushal, T. M. Shylaja, S. S. PES Univ Bangalore Karnataka India

ISBN: (纸本)9798350320565

A hazy image is one where atmospheric effects degrade the contrast and visibility of the image. It is often caused by the dispersion of light into the moisture particles present, smoke etc. This results in lower performance in high level vision tasks such as object detection, free space detection, scene understanding, etc. Hence the images have to be dehazed before applying other high level algorithms. Dehazing is the process of reconstructing the original colour and contrast of the image if taken in normal conditions. image dehazing is a non-trivial task as it is hard to collect haze free ground truth images. Further, achieving dehazed images when variable haze is present is a significantly harder challenge. In this research, we propose the Non Homogeneous RESIDE dataset (NH-RESIDE) that contains images created synthetically using the principles of randomness and representativeness. Experimental results show that the model trained on our dataset produces visually more pleasing images with a much better dehazing effect on real world images. The model implemented in this paper also outperforms the state-of-the-art models by a huge margin on the NH-Haze dataset proposed by the NTIRE Non Homogeneous Dehazing Challenge at CVPR, achieving an average PSNR of 25.69 and an average SSIM of 0.80. It also achieves much better processing times when compared to other models, thereby facilitating real-time performance.

关键词： Computer vision image color analysis conferences Moisture Object detection Computer architecture Real-time systems

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YOLOv9 in Action: A Futuristic Approach to Real-Time License Plate Recognition and Analysis

YOLOv9 in Action: A Futuristic Approach to Real-Time License...

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2024 IEEE International conference on Intelligent Signal processing and Effective Communication Technologies, INSPECT 2024

作者： Kumar, M Ranjith Sendur, G Jeevan Kumar, S Praveen Annamalai, R. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai600127 India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350379525

The automated recognition and identification of license plates is an essential element of intelligent transportation systems that enable effective traffic management, security measures, and the development of efficient e-payment algorithms in toll booths and parking facilities. Even though license plate recognition (LPR) technology is fairly advanced, significant efforts are being made to enhance its accuracy and resilience The growing adoption of deep learning technologies across multiple industry domains, particularly computer vision, presents a novel idea implemented in this paper. To develop an approach to improve detection accuracy using YOLOv9 and transfer learning. Through combining the power of YOLOv9 with transfer learning, it is feasible to tackle the traditional LPR system's inherent flaws. Character recognition, picture pre-processing, license plate extraction, and character segmentation are the four main parts of the proposed system. Each step involves unique methodologies, primarily focusing on character recognition methodologies using YOLOv9 which has not been adequately tackled in existing research. By thorough testing and analysis, this study seeks to further the current developments in LPR systems, ensuring discernibly better-established intelligent transportation solutions. We have achieved a mAP50 Score of 99.5% and a mAP50-95 score of 71 % © 2024 IEEE.

关键词： image segmentation

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Research on Nonlinear Programming Problems Based on Random Forest Model and Genetic Algorithm

Research on Nonlinear Programming Problems Based on Random F...

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image processing, Electronics and Computers (IPEC), Asia-Pacific conference on

作者： Yanhui Li Yuhua Qing Wentao Huang Kun Qi Ke Du Guodong Li School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin P. R. China School of Foreign Languages Guilin University of Electronic Technology Guilin P. R. China School of Mechanical Engineering Xi’an Aeronautical University Xi’an P. R. China

ISBN: (数字)9798350374407

ISBN: (纸本)9798350374414

In the realm of daily life planning, the challenge of optimizing decisions is frequently encountered, with traditional methods often failing to achieve desired outcomes. To address this issue, this research proposes an innovative approach that combines random forest and genetic algorithms to obtain optimal solutions for nonlinear programming problems. The aim is to enhance the precision of solving nonlinear programming problems and improve the effectiveness of decision optimization in daily life planning. The research introduces important constraints on influencing factors in the design of the random forest model for nonlinear programming and utilizes genetic algorithms to solve the nonlinear programming problems, resulting in more accurate optimal solutions. The research findings demonstrate that this method not only optimizes the impact structure of various factors on the outcomes but also enhances the precision of the optimal solutions through genetic algorithms.

关键词： Accuracy Computational modeling image processing Optimization methods Programming Iterative algorithms Planning Reliability Random forests Genetic algorithms

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Mobile-Agent-v2: Mobile Device Operation Assistant with Effective Navigation via Multi-Agent Collaboration 38

Mobile-Agent-v2: Mobile Device Operation Assistant with Effe...

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38th conference on Neural Information processing systems, NeurIPS 2024

作者： Wang, Junyang Xu, Haiyang Jia, Haitao Zhang, xi Yan, Ming Shen, Weizhou Zhang, Ji Huang, Fei Sang, Jitao Beijing Jiaotong University China Alibaba Group China

Mobile device operation tasks are increasingly becoming a popular multi-modal AI application scenario. Current Multi-modal Large Language Models (MLLMs), constrained by their training data, lack the capability to function effectively as operation assistants. Instead, MLLM-based agents, which enhance capabilities through tool invocation, are gradually being applied to this scenario. However, the two major navigation challenges in mobile device operation tasks - task progress navigation and focus content navigation - are difficult to effectively solve under the single-agent architecture of existing work. This is due to the overly long token sequences and the interleaved text-image data format, which limit performance. To address these navigation challenges effectively, we propose Mobile-Agent-v2, a multi-agent architecture for mobile device operation assistance. The architecture comprises three agents: planning agent, decision agent, and reflection agent. The planning agent condenses lengthy, interleaved image-text history operations and screens summaries into a pure-text task progress, which is then passed on to the decision agent. This reduction in context length makes it easier for decision agent to navigate the task progress. To retain focus content, we design a memory unit that updates with task progress by decision agent. Additionally, to correct erroneous operations, the reflection agent observes the outcomes of each operation and handles any mistake accordingly. Experimental results indicate that Mobile-Agent-v2 achieves over a 30% improvement in task completion compared to the single-agent architecture of Mobile-Agent. The code is open-sourced at https://***/X-PLUG/MobileAgent. © 2024 Neural information processing systems foundation. All rights reserved.

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Invisible Backdoor Attack with image Contours Triggers 24th

Invisible Backdoor Attack with Image Contours Triggers

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24th International conference on algorithms and Architectures for Parallel processing, ICA3PP 2024

作者： Hou, Yangzhi Yang, Ying Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9789819615445

In recent years, backdoor attack techniques on neural networks have been widely studied and researched. In this attack mode, the model implanted with a backdoor behaves normally when processing normal inputs, but once it encounters a special trigger designed by the attacker, its prediction results will be maliciously manipulated. Different backdoor attack strategies have been proposed to improve the stealth and robustness of the triggers. However, most of the existing backdoor attack strategies use fixed triggering patterns or input-independent triggering patterns, and thus are easily detected and blocked by existing defense systems. To address these limitations, we propose a backdoor attack method based on image contours. Specifically, we first use image contours as the target region for backdoor injection and fill them with specific color information to generate trigger patterns. Then, using our designed Generative Network (GN) and the Auxiliary Extraction Network (AE), the trigger patterns are covertly embedded into the images to be contaminated to generate a poisoned dataset, which contaminates the training model for the purpose of malicious attacks. Compared with traditional methods, our trigger patterns are more natural and harder to detect. Experimental results show that our proposed attack method achieves excellent performance in terms of both stealth and robustness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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Techniques for Pre-processing CT Angiography (CTA) images for the Diagnosis of Cardiac Disease 2

Techniques for Pre-processing CT Angiography (CTA) Images fo...

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2nd International conference on Augmented Intelligence and Sustainable systems, ICAISS 2023

作者： Punitha, T. Santhi Preethika, S K. Piramu Vistas School of Computing Sciences Chennai India

ISBN: (纸本)9798350325799

Artificial Intelligence (AI) is a rapidly developing discipline that concentrates on teaching computers to comprehend and scrutinize images, particularly in the medical field, with a specific emphasis on diagnosing Cardiovascular Diseases. Coronary Computed Tomography Angiography (CCTA) is a widely accepted non-invasive diagnostic examination used to evaluate cardiac disease (CD). CCTA images are coronary artery anatomy, whereas functional stress testing assesses for inducible cardiac ischemia. Nevertheless, CCTA images frequently encounter diverse forms of noise and artifacts that can deteriorate their quality and hinder precise diagnosis. Preprocessing CT Angiography images is a vital undertaking in medical diagnosis utilizing computer vision. This study commences by employing image enhancement preprocessing methods to diminish noise, blurring, and image weakening. The initial stages involve utilizing edge detection algorithms and smoothing/filtering functions to enhance pixel clarity and edge definition in CCTA images using Python programming language and the PyCharm Integrated Development Environment (IDE) are implemented in this work. © 2023 IEEE.

关键词： image enhancement

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Realtime Detection of Humps and Potholes

Realtime Detection of Humps and Potholes

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2023 International conference on Smart systems for Applications in Electrical Sciences, ICSSES 2023

作者： Tejas, B.S. Pavan, V. Rohith, H. Pranam, J. Hegde, Veena N. Bms College of Engineering Dept. of Electronics and Instrumentation Engineering Bengaluru India

ISBN: (纸本)9798350347296

Hump and pothole detection is essential for ensuring road safety and preventing damage to vehicles. In recent years, there has been a growing interest in developing automated methods for hump and pothole detection. This paper presents the detection of humps and potholes using techniques of image processing, machine learning and sensor-based approach. The proposed method involves the use of cameras and sensors to collect data on road conditions, analyze the data and identify areas with humps or potholes. Machine learning algorithms are applied learn to recognize patterns in the data and make accurate predictions. The solution provided is robust with an accuracy of 90% approximately in achieving the objective under different lighting and weather conditions. The proposed solution is implemented in real time and tested for different conditions. The analysis is carried out in terms of improved road safety and reduced maintenance costs using the proposed solution. © 2023 IEEE.

关键词： image processing

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An application of smart visual air conditioning controller to enhance control efficiency in office environments

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RESULTS IN ENGINEERING 2025年 25卷

作者： Tian, Yu Zhao, Zilong Loo, Junn Yong Univ Malaya Fac Engn Dept Mech Engn Kuala Lumpur 50603 Malaysia Monash Univ Malaysia Sch Informat Technol Jalan Lagoon Selatan 47500 Bandar Sunway Selangor Darul Malaysia

Innovative solutions for sustainability and energy efficiency are crucial in green building management. This study presents a novel approach to optimizing air conditioning (AC) system operations in commercial buildings, with a focus on real-time control aimed at reducing energy consumption. We propose the Smart Visual Air Conditioning Controller (SVACC), which utilizes computer vision and deep learning-based human detection to intelligently manage AC operation, minimizing unnecessary runtime. By detecting human presence in meeting rooms, the system dynamically adjusts AC activation based on occupancy, thereby significantly reducing energy waste. A statistical analysis conducted over five months across ten conference rooms demonstrated that the SVACC reduced AC usage time by 33.60 %. We validate and optimize the SVACC across various building types, including commercial office spaces, industrial warehouses and laboratories, and residential apartments. The system achieved an optimal balance with 96.55 % precision and 93.33 % recall, resulting in an F1 score of 0.9492, demonstrating high performance across various environments. Our results underscore the effectiveness of the SVACC, which highlights the potential of integrating advanced deep learning models with HVAC systems to optimize energy consumption. This approach offers a promising solution for improving HVAC design and energy management across diverse building environments. Future work will focus on refining sensor technology and control algorithms to further optimize energy efficiency.

关键词： HVAC Building management Deep learning image processing Computer vision

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An improved YOLOv8-based vehicle detection model for rainy weather conditions 9

An improved YOLOv8-based vehicle detection model for rainy w...

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9th International conference on Computer and Communication systems (ICCCS)

作者： Lin, Dapeng Qu, Licheng Yue, Wenqi Wang, Jian Yu, Yanjie Changan Univ Sch Transportat Engn Xian Peoples R China Changan Univ Sch Informat Engn Xian Peoples R China

ISBN: (纸本)9798350350227;9798350350210

In light of the limited detection accuracy and susceptibility to missed detections exhibited by most algorithms under rainy conditions, a rain-day vehicle target detection model based on improved YOLOv8 is proposed. Firstly, PIGWM is used to preprocess the original image for rain removal, and parameter importance-guided weight modification is employed to adjust network weights to address the performance degradation issue of deep learning models when processing incremental datasets, thereby improving the rain removal performance of images. Then, SlideLoss sliding loss function is introduced to enable the model to adaptively learn the threshold parameters of positive samples and negative samples, solving the imbalance problem between different samples and enhancing detection accuracy. Finally, CPCA attention mechanism is incorporated into the Neck feature fusion network to enhance the model's feature fusion capability. Experimental results on the self-built KITTI-RAIN dataset show that the improved algorithm achieves higher accuracy compared to the original model, with accuracy increasing from 92.6% to 94.5%, recall increasing from 82.9% to 87.6%, average precision increasing from 91.4% to 94.1%, and P, R, mAP increasing by 1.9%, 4.7%, and 2.7% respectively, demonstrating its effectiveness in adapting to vehicle detection tasks in rainy conditions.

关键词： yolov8,loss function image de-raining,feature fusion attention mechanism

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