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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： R. Premalatha Somasundaram R Prashant Atmakuri G.G. Sreeja Pooja P. Raj Loveleen Kumar Department of Microbiology CMS College of Science and Commerce Coimbatore Tamil Nadu India Department of Management Studies Kongu Engineering College Perundurai Erode Tamil Nadu India Department of CSE Prasad V Potluri Siddhartha Institute of Technology Vijayawada Andhra Pradesh India Department of Computer and Communication Engineering Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of CSE Dr. C.V Raman University Bilaspur C.G India Department of Computer Science & Engineering Swami Keshvanand Institute of Technology Management & Gramothan Jaipur Rajasthan India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

When it comes to discovering and eliminating pests, traditional methods are typically laborious, time-consuming, and hazardous to the environment. In recent years, deep literacy methods have emerged as interesting and potentially useful technologies for automating the finding of pests in agricultural fields. The purpose of this work is to provide a detailed evaluation of the operation of deep literacy methods in automated pest discovery. These methods include a variety of characteristics such as image processing, point birth, and bracket algorithms. In this paper, we discuss the difficulties and opportunities that are involved with the implementation of deep literacy-based pest discovery systems. These problems and opportunities include dataset reflection, model training, and scalability. In addition to this, we highlight recent developments, case stories, and new exploration directions in this rapidly developing field. Automated pest discovery systems have the potential to enhance agricultural operations by utilizing the powers of deep literacy. This would allow for the prompt and precise identification of pests, which would be necessary for the development of effective pest management techniques. Cutting-edge technology Within the scope of this investigation, Yolov3-Tiny, Yolov4, Yolov4-Tiny, Yolov6, and Yolov8 were utilized for detection. For object detection, each of these models was selected. images have been marked with Yolo. Yolov8 outperformed previous trials by having the highest mAP of 84.7% and an average loss of 0.7939 for the detection of tiny pests. An Android application that offered real-time insect detection utilized the Yolov8 paradigm. The purpose of this research is to develop and deploy novel deep-learning models, analytical tools, and a procedure for identifying crop pests for pest management.

关键词： Training deep learning Geography Insects Scalability Machine vision Object detection Robustness Reflection real-time systems

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Dronexscan: Detection and Identification of Vehicles in Aerial images Captured by Unmanned Aerial Vehicles(UAVs)

Dronexscan: Detection and Identification of Vehicles in Aeri...

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International Conference on Communication and Electronics Systems (ICCES)

作者： J.V. Anchitaalagammai S. Kavitha S. Murali Rlk. Sarvesh S. Seshanand A. Javanesh Computer Science and Engineering Velammal College of Engineering and Technology Madurai India Computer Science and Engineering Velammal College of Engineering and Technology. (of Affiliation) Madurai India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

This research study proposes Drone-TOOD, the low-power consumption task-aligned object detection algorithm for vehicle detection that was designed from the images captured by UAVs. In the aim of bringing low-power, real-time use into this methodfrom the ESP32-CAM image-processing module, it brings the principles of TOOD into it to balance precision and velocity while detecting vehicles through aerial views and challenges such as variation in altitude, lighting conditions, and orientations of the vehicles. In this regard, the low computational capabilities of ESP32-CAM are supplemented by optimized deep learning methods offering high performance in effective detection with minimal power consumption. Drone-TOOD has performed outstandingly well within the domain of UA V-based vehicle detection and hence asserts suitability for surveillance, traffic monitoring, and search-and-rescue applications.

关键词： Technological innovation Machine learning algorithms Surveillance Vehicle detection Traffic control Autonomous aerial vehicles Robustness real-time systems Sensors Object recognition

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Nighttime object detection system with lightweight deep network for internet of vehicles

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JOURNAL OF real-time image processing 2021年第4期18卷 1141-1155页

作者： Jhong, Sin-Ye Chen, Yung-Yao Hsia, Chih-Hsien Lin, Shih-Chang Hsu, Kuo-Hua Lai, Chin-Feng Natl Cheng Kung Univ Dept Engn Sci Tainan Taiwan Natl Taiwan Univ Sci & Technol Dept Elect & Comp Engn Taipei Taiwan Natl Taipei Univ Technol Grad Inst Automat Technol Taipei Taiwan

Autonomous driving systems in internet of vehicles (IoV) applications usually adopt a cloud computing mode. In these systems, information got at the edge of the cloud computing center for data analysis and situation response. However, the conventional IoV face enormous challenges to meet the requirements in terms of storage, communication, and computing problems because of the considerable amount of information on the traffic environment. The environment perception during the nighttime is poorer than that during the daytime that this problem also requires addressing. To solve these problems, we propose a nighttime object detection scheme based on a lightweight deep learning model in the edge computing mode. First, the pedestrian detection and the vehicle detection algorithm that using the thermal images based on the YOLO architecture. We can implement the model on edge devices that can achieve real-time detection through the designed lightweight strategy. Next, a spatial prior information and temporal prior information into the detection algorithm and divide the frames into key and non-key frames to increase the performance and speed of the system simultaneously. Finally, we implemented the detection network for performance and feasibility verification on the Jetson TX2 edge device. The experimental results show that the proposed system can achieve real-time and high-accuracy object detection on edge devices.

关键词： Internet of vehicles Thermal infrared images Object detection deep neural network Prior information

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Boosted-DEPICT: an effective maize disease categorization framework using deep clustering

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NEURAL COMPUTING & APPLICATIONS 2020年 1-10页

作者： Gokulnath, B. V. Usha Devi, G. Vellore Inst Technol Sch Informat Technol & Engn Vellore Tamil Nadu India

Clustering of plant disease from digital images is an arduous task due to its dynamic nature and change of appearance under different environmental conditions. In most cases, the image captured in the real-time scenario is subjected to added noise, distortion, poor lighting conditions, and other potential factors that results in poor model performance during the process of discriminating between normal and disease-affected samples. It eventually maximizes the margin of the error rate, thereby leading to misclassification of disease of different varieties of plants in the database with other categories. This paper presents an effective deep clustering-based plant disease categorization algorithm, Boosted-deep Embedded Regularized Clustering (DEPICT). This model integrates the convolutional autoencoder model with locality-preserving constraints and group sparsity into the network, which improves the embedded learning representation of the images. The PlantVillage and PDD image databases are accessed to develop this model for maize crop. The images are segmented by eliminating the background, cropped, augmented before model training. The performance of the system is evaluated by clustering accuracy and normalized mutual information. The proposed Boosted-DEPICT exhibits better performance, attains promising results with an accuracy of 97.73% and 91.25% on PV and PDD datasets, and outperforms state-of-the-art deep clustering algorithms. This system could be further enhanced by automating the entire process and transforming it into a mobile application for real-time analysis to gain instant results from any region.

关键词： Autoencoders Boosted-DEPICT deep clustering image processing Plant disease

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Scale Efficient Training for Large Datasets

arXiv

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arXiv 2025年

作者： Zhou, Qing Gao, Junyu Wang, Qi Northwestern Polytechnical University China

The rapid growth of dataset scales has been a key driver in advancing deep learning research. However, as dataset scale increases, the training process becomes increasingly inefficient due to the presence of low-value samples, including excessive redundant samples, overly challenging samples, and inefficient easy samples that contribute little to model improvement. To address this challenge, we propose Scale Efficient Training (SeTa) for large datasets, a dynamic sample pruning approach that losslessly reduces training time. To remove low-value samples, SeTa first performs random pruning to eliminate redundant samples, then clusters the remaining samples according to their learning difficulty measured by loss. Building upon this clustering, a sliding window strategy is employed to progressively remove both overly challenging and inefficient easy clusters following an easy-to-hard curriculum. We conduct extensive experiments on large-scale synthetic datasets, including ToCa, SS1M, and ST+MJ, each containing over 3 million samples. SeTa reduces training costs by up to 50% while maintaining or improving performance, with minimal degradation even at 70% cost reduction. Furthermore, experiments on various scale real datasets across various backbones (CNNs, Transformers, and Mambas) and diverse tasks (instruction tuning, multi-view stereo, geo-localization, composed image retrieval, referring image segmentation) demonstrate the powerful effectiveness and universality of our approach. Code is available at https://***/mrazhou/SeTa. © 2025, CC BY-NC-SA.

关键词： Stereo image processing

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Early real-time detection algorithm of tomato diseases and pests in the natural environment

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PLANT METHODS 2021年第1期17卷 1-17页

作者： Wang, Xuewei Liu, Jun Zhu, Xiaoning Weifang Univ Sci & Technol Shandong Prov Univ Lab Protected Hort Block Chain Lab Agr Vegetables Weifang 262700 Shandong Peoples R China Elite Digital Intelligence Technol Co LTD Beijing Peoples R China

Background Research on early object detection methods of crop diseases and pests in the natural environment has been an important research direction in the fields of computer vision, complex image processing and machine learning. Because of the complexity of the early images of tomato diseases and pests in the natural environment, the traditional methods can not achieve real-time and accurate detection. Results Aiming at the complex background of early period of tomato diseases and pests image objects in the natural environment, an improved object detection algorithm based on YOLOv3 for early real-time detection of tomato diseases and pests was proposed. Firstly, aiming at the complex background of tomato diseases and pests images under natural conditions, dilated convolution layer is used to replace convolution layer in backbone network to maintain high resolution and receptive field and improve the ability of small object detection. Secondly, in the detection network, according to the size of candidate box intersection ratio (IOU) and linear attenuation confidence score predicted by multiple grids, the obscured objects of tomato diseases and pests are retained, and the detection problem of mutual obscure objects of tomato diseases and pests is solved. Thirdly, to reduce the model volume and reduce the model parameters, the network is lightweight by using the idea of convolution factorization. Finally, by introducing a balance factor, the small object weight in the loss function is optimized. The test results of nine common tomato diseases and pests under six different background conditions are statistically analyzed. The proposed method has a F1 value of 94.77%, an AP value of 91.81%, a false detection rate of only 2.1%, and a detection time of only 55 Ms. The test results show that the method is suitable for early detection of tomato diseases and pests using large-scale video images collected by the agricultural Internet of Things. Conclusions At present, most of

关键词： real-time detection algorithm deep learning Dilated convolution NMS YOLOv3 Tomato diseases and pests Natural environment

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deep learning for real-time Leaf Disease Detection: Revolutionizing Apple Orchard Health

Deep Learning for Real-Time Leaf Disease Detection: Revoluti...

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Big Data Analytics and Practices (IBDAP), International Conference on

作者： Maherun Nessa Isty Nishat Vasker Fahmida Anjum Warda Ruhin Parsub Amit Roy Akibul Haque Mahamudul Hasan Maheen Islam Department of Computer Science and Engineering East West University Dhaka Bangladesh

deep learning approach, particularly integrated neural architecture for real-time Single Shot multibox detector (INAR-SSD), is the most effective approach in the context of fast detection and classification of apple leaf diseases in apple orchards. Advanced data augmentation and complex image annotation techniques are using in the development of apple leaf diseases detection dataset (ALDD), which is the main purpose of this study. Apple leaf diseases detection dataset plays a vital role in the field of identifying apple leaf disease which is the key challenge of this study. The novel INAR-SSD model, created by the combination of SSD architecture with an integrated neural architecture, is proposed in this research. Utilizing the various functionalities of GoogLeNet, Inception Structure and Rainbow concentration the innovative INAR-SSD model is trained to identify various types of apple leaf diseases that effect on apple leaves. To categorize the diseases precisely, the INAR-SSD model follows digital image processing such as noise removing, overfitting reducing, feature extraction etc. Another purpose of study is to report the findings from the experiments that evaluate the performance of the proposed model. The INAR-SSD model successfully detects the illnesses in apple leaves with an accuracy of 97%, evidenced by its mean Average Precision(mAP), and the processing speed of 23.13 Frames Per Second (FPS).

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A TinyML based Residual Binarized Neural Network for real-time image Classification

A TinyML based Residual Binarized Neural Network for real-ti...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： C Srinivasan P Sridhar V Hari Priya S Swathi Department of ECE Saveetha Engineering College Chennai Tamilnadu India Department of ECE Sri Ramakrishna Engineering College Coimbatore Tamilnadu India

ISBN: (纸本)9781665482721

image processing is an important requirement in IoT applications such as robotics, augmented reality, computer vision, Industry 4.0 etc. The capabilities of IoT devices for image processing are limited to sensing the environment, processing and communicating the results. Tiny machine learning (TinyML) is a new paradigm that takes advantage of the IoT to deploy deep learning models to perform complex tasks in resource constrained embedded devices. image classification is an important task in IoT to interpret images of a particular scene or class. Currently, this task is performed in embedded devices using Binarized Neural Networks (BNNs), which can be converted to a set of weights using a one-hot encoding process. These networks integrated with hardware accelerators can be trained to perform image processing tasks in real-time. This paper proposes a BNN for image classification based on residual learning paradigm, called Tiny-BNN which exploits the skip connections to reduce information loss, and improve the training time and accuracy. Experimental results show that the model achieves a classification accuracy of 90.1 % and 91.6% on the on CIFAR-10 and MNIST datasets respectively.

关键词： Performance evaluation deep learning Computer vision Computational modeling Neural networks Robot sensing systems real-time systems

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WB LUTS: CONTRASTIVE learning FOR WHITE BALANCING LOOKUP TABLES

arXiv

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arXiv 2024年

作者： Manne, Sai Kumar Reddy Wan, Michael Roux Institute Northeastern University United States Institute for Experiential AI Northeastern University United States

Automatic white balancing (AWB), one of the first steps in an integrated signal processing (ISP) pipeline, aims to correct the color cast induced by the scene illuminant. An incorrect white balance (WB) setting or AWB failure can lead to an undesired blue or red tint in the rendered sRGB image. To address this, recent methods pose the post-capture WB correction problem as an image-to-image translation task and train deep neural networks to learn the necessary color adjustments at a lower resolution. These low resolution outputs are post-processed to generate high resolution WB corrected images, forming a bottleneck in the end-to-end run time. In this paper we present a 3D Lookup Table (LUT) based WB correction model called WB LUTs that can generate high resolution outputs in real time. We introduce a contrastive learning framework with a novel hard sample mining strategy, which improves the WB correction quality of baseline 3D LUTs by 25.5%. Experimental results demonstrate that the proposed WB LUTs perform competitively against state-of-the-art models on two benchmark datasets while being 300× faster using 12.7× less memory. Our model and code are available at https://***/skrmanne/3DLUT_sRGB_WB1 © 2024, CC BY.

关键词： Table lookup

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Non-Invasive Data Acquisition and IoT Solution for Human Vital Signs Monitoring: Applications, Limitations and Future Prospects

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SENSORS 2022年第17期22卷 6625页

作者： Salem, Mahmoud Elkaseer, Ahmed El-Maddah, Islam A. M. Youssef, Khaled Y. Scholz, Steffen G. Mohamed, Hoda K. Karlsruhe Inst Technol Inst Automat & Appl Informat D-76344 Eggenstein Leopoldshafen Germany Karlsruhe Inst Technol Karlsruhe Nano Micro Facil D-76344 Eggenstein Leopoldshafen Germany Port Said Univ Fac Engn Port Said 42526 Egypt Ain Shams Univ Fac Engn Cairo 11535 Egypt Beni Suef Univ Fac Nav Sci & Space Technol Bani Suwayf 2731070 Egypt Swansea Univ Coll Engn Swansea SA2 8PP W Glam Wales

The rapid development of technology has brought about a revolution in healthcare stimulating a wide range of smart and autonomous applications in homes, clinics, surgeries and hospitals. Smart healthcare opens the opportunity for a qualitative advance in the relations between healthcare providers and end-users for the provision of healthcare such as enabling doctors to diagnose remotely while optimizing the accuracy of the diagnosis and maximizing the benefits of treatment by enabling close patient monitoring. This paper presents a comprehensive review of non-invasive vital data acquisition and the Internet of Things in healthcare informatics and thus reports the challenges in healthcare informatics and suggests future work that would lead to solutions to address the open challenges in IoT and non-invasive vital data acquisition. In particular, the conducted review has revealed that there has been a daunting challenge in the development of multi-frequency vital IoT systems, and addressing this issue will help enable the vital IoT node to be reachable by the broker in multiple area ranges. Furthermore, the utilization of multi-camera systems has proven its high potential to increase the accuracy of vital data acquisition, but the implementation of such systems has not been fully developed with unfilled gaps to be bridged. Moreover, the application of deep learning to the real-time analysis of vital data on the node/edge side will enable optimal, instant offline decision making. Finally, the synergistic integration of reliable power management and energy harvesting systems into non-invasive data acquisition has been omitted so far, and the successful implementation of such systems will lead to a smart, robust, sustainable and self-powered healthcare system.

关键词： healthcare system vital signs vital data non-invasive data acquisition internet of things (IoT) machine-to-machine (M2M) communication wireless sensor network (WSN) digital image processing computer vision

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