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image processing, Computer Vision and Machine learning (ICICML), International Conference on

作者： Shiyang Wu Chunyang Zhao Leda Qu College of Computer Science and Engineering Shenyang Ligong University Shenyang China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Despite the great success of deep learning in object detection at present, its performance and efficiency for small target detection in UAV images are still unsatisfactory. In order to improve the performance of small target detection, which proposes an algorithm MS-YOLO based on the YOLOv8 algorithm to improve the backbone network and the detection head. Firstly, the SPD (Space-To-Depth Convolution), which is a feature extraction layer suitable for low-resolution images and small target images, is integrated into the shallow layer of the backbone network, which does not use stepwise convolution and pooling to better retain the small target feature information. This layer does not use step-width convolution and pooling, which can better retain the small target feature information. Secondly, a detection header specialized for the small target feature layer is added. The improved algorithm achieves state-of-the-art detection results on the VisDrone dataset with an average precision (AP) of 46.5 and 28.0, respectively, and a model parameter count of 16.8M, which improves the detection accuracy while maintaining lightweight.

关键词： Accuracy image resolution Convolution Object detection Autonomous aerial vehicles Feature extraction real-time systems Magnetic heads Marine vehicles Faces

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Dynamic and real-time Object Detection Based on deep learning for Home Service Robots

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SENSORS 2023年第23期23卷 9482-9482页

作者： Ye, Yangqing Ma, Xiaolon Zhou, Xuanyi Bao, Guanjun Wan, Weiwei Cai, Shibo Zhejiang Univ Technol Coll Mech Engn Hangzhou 310023 Peoples R China China Jiliang Univ Coll Mech & Elect Engn Hangzhou 310018 Peoples R China Osaka Univ Grad Sch Engn Sci Suita 5620045 Japan

Home service robots operating indoors, such as inside houses and offices, require the real-time and accurate identification and location of target objects to perform service tasks efficiently. However, images captured by visual sensors while in motion states usually contain varying degrees of blurriness, presenting a significant challenge for object detection. In particular, daily life scenes contain small objects like fruits and tableware, which are often occluded, further complicating object recognition and positioning. A dynamic and real-time object detection algorithm is proposed for home service robots. This is composed of an image deblurring algorithm and an object detection algorithm. To improve the clarity of motion-blurred images, the DA-Multi-DCGAN algorithm is proposed. It comprises an embedded dynamic adjustment mechanism and a multimodal multiscale fusion structure based on robot motion and surrounding environmental information, enabling the deblurring processing of images that are captured under different motion states. Compared with DeblurGAN, DA-Multi-DCGAN had a 5.07 improvement in Peak Signal-to-Noise Ratio (PSNR) and a 0.022 improvement in Structural Similarity (SSIM). An AT-LI-YOLO method is proposed for small and occluded object detection. Based on depthwise separable convolution, this method highlights key areas and integrates salient features by embedding the attention module in the AT-Resblock to improve the sensitivity and detection precision of small objects and partially occluded objects. It also employs a lightweight network unit Lightblock to reduce the network's parameters and computational complexity, which improves its computational efficiency. Compared with YOLOv3, the mean average precision (mAP) of AT-LI-YOLO increased by 3.19%, and the detection precision of small objects, such as apples and oranges and partially occluded objects, increased by 19.12% and 29.52%, respectively. Moreover, the model inference efficiency had a 7 ms red

关键词： real-time object detection indoor service robots DA-Multi-DCGAN AT-LI-YOLO

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deep learning and Machine learning Based Efficient Framework for image Based Plant Disease Classification and Detection

Deep Learning and Machine Learning Based Efficient Framework...

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2022 International Conference on Advanced Computing Technologies and Applications, ICACTA 2022

作者： Nancy, P. Pallathadka, Harikumar Naved, Mohd Kaliyaperumal, Karthikeyan Arumugam, K. Garchar, Vipul Saveetha Institute of Medical and Technical Sciences Chennai India Manipur International University Manipur India Amity University Noida India AMBO University IT HH Campus India Karpagam Academy of Higher Education Coimbatore India Junagadh Agricultural University Junagadh India

ISBN: (纸本)9781665495158

Without agriculture, human existence would be inconceivable. A large percentage of the world's population relies on agriculture for their daily needs. In addition, it creates a big number of jobs in the area. Using traditional agricultural practices results in lower yields, which is the fault of farmers. Agriculture and allied sectors will continue to be critical to the economy's long-term growth and prosperity. Farming has a slew of challenges, including disease detection and control and crop monitoring and tracking. Farming with intelligence is a realistic option in many situations. Smart agriculture is now possible because to the internet of things and machine learning approaches. Computer vision, image processing, and machine learning techniques are used in the automated leaf disease diagnostic system to analyze photographs of diseased leaves. A farmer can make an educated choice regarding a plant illness thanks to automated disease detection equipment that speeds up the diagnostic process. A farmer had to first send the contaminated leaf to a pathology lab for confirmation of the illness, which was a tedious process. It is the purpose of this paper to propose a framework for the real-time classification of agricultural images. Crop disease pictures categorization and illness prediction are made easier using this system. © 2022 IEEE.

关键词： image segmentation

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The Comprehensive Review on deep learning-Based Technique for Potato Disease

SSRN

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SSRN 2024年

作者： Singh, Animesh Badhan, Ajay Kumar Kumar, Shlok Meher, Markanda Kumar Acharya, Biswanath Rajendra Ratna, Shrey Sunil School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India

The potato is grown worldwide and is the fourth largest food crop. Each of us knows the potato as a vegetable. If we look at other countries, it is clear that the potato is the most popular vegetable in the world, as several agricultural offices repeatedly state. Since fungi are the main cause of infection of potato plants, they are affected by both late blight and tuber blight. Plant diseases have a major impact on crop yields, so plant diseases need to be identified and understood. real-time disease management and control will improve yield and reduce agricultural losses. The proposed model would effectively detect and analyze potato leaf illnesses with image processing techniques. The CNN-based model is used in this study to detect illnesses from potato leaf photos because CNN is used for image classification and achieves improvements over other machine-learning algorithms. This paper suggests a CNN-based architecture specifically designed for the recognition of potato illnesses. image-processing is used to create a database for the training set. Thus, high precision can be achieved. © 2024, The Authors. All rights reserved.

关键词： Vegetables

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Uncertainty Quantified deep learning and Regression Analysis Framework for image Segmentation of Skin Cancer Lesions

arXiv

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arXiv 2024年

作者： Elfatimi, Elhoucine Shah, Pratik Department of Pathology Laboratory Medicine University of California IrvineCA United States Department of Pathology Laboratory Medicine Biomedical Engineering University of California IrvineCA United States

deep learning models (DLMs) frequently achieve accurate segmentation and classification of tumors from medical images. However, DLMs lacking feedback on their image segmentation mechanisms such as Dice coefficients and confidence in their performance face challenges processing previously unseen images in real-world clinical settings. Uncertainty estimates to identify DLM predictions at the cellular or single-pixel levels requiring clinician review can enhance trust, however their deployment requires significant computational resources. This study reports two DLMs, one trained from scratch and another based on transfer learning, with Monte Carlo dropout or Bayes-by-backprop uncertainty estimations to segment lesions from the publicly available The International Skin Imaging Collaboration-19 dermoscopy image database with lesions of cancer. A novel approach to compute pixel-by-pixel uncertainty estimations of DLM segmentation performance in multiple clinical regions from a single dermatoscopy image with corresponding Dice scores is reported for the first time. image-level uncertainty maps demonstrated correspondence between imperfect DLM segmentation and high uncertainty levels in specific skin tissue regions with or without lesions. Four new linear regression models that can predict the Dice performance of DLM segmentation using constants and uncertainty measures either individually or in combination from lesions, tissue structures, and non-tissue pixels regions critical for clinical diagnosis and prognostication in skin images (Spearman’s correlation, p Copyright © 2024, The Authors. All rights reserved.

关键词： image segmentation

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4th International Conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2021

4th International Conference on Recent Trends in Image Proce...

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4th International Conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2021

ISBN: (纸本)9783031070044

The proceedings contain 33 papers presendted at a virtual meeting. The special focus in this conference is on Recent Trends in image processing and Pattern Recognition. The topics include: real-time Face Recognition for Organisational Attendance Systems;Harnessing Sustainable Development in image Recognition Through No-Code AI Applications: A Comparative Analysis;evaluating Performance of Adam Optimization by Proposing Energy Index;an Alignment-Free Fingerprint Template Protection Technique Based on Minutiae Triplets;early Prediction of Complex Business Processes Using Association Rule Based Mining;A Framework for Masked-image Recognition System in COVID-19 Era;A deep-learning Based Automated COVID-19 Physical Distance Measurement System Using Surveillance Video;Detection of Male Fertility Using AI-Driven Tools;face Mask Detection Using deep Hybrid Network Architectures;a Super Feature Transform for Small-Size image Forgery Detection;UHTelHwCC: A Dataset for Telugu Off-line Handwritten Character Recognition;inflectional and Derivational Hybrid Stemmer for Sentiment Analysis: A Case Study with Marathi Tweets;adaptive Threshold-Based Database Preparation Method for Handwritten image Classification;a Graph-Based Holistic Recognition of Handwritten Devanagari Words: An Approach Based on Spectral Graph Embedding;Imagined Object Recognition Using EEG-Based Neurological Brain Signals;a Fast and Efficient K-Nearest Neighbor Classifier Using a Convex Envelope;single Channel Speech Enhancement Using Masking Based on Sinusoidal Modeling;extraction of Temporal Features on Fibonacci Space for Audio Based Vehicle Classification;an Empirical Study of Vision Transformers for Cervical Precancer Detection;An Improved Technique for Preliminary Diagnosis of COVID-19 via Cough Audio Analysis;agricultural Field Analysis Using Satellite Hyperspectral Data and Autoencoder;Development of NDVI Prediction Model Using Artificial Neural Networks;time Series Forecasting of Soil Moisture Using Sa

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Improved real-time Yoga Pose Estimation with GAN Augmentation

Improved Real-time Yoga Pose Estimation with GAN Augmentatio...

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Advances in Signal processing, Power, Communication, and Computing (ASPCC), IEEE International Conference on

作者： Kappa Dinesh Reddy Kalivarapu Sai Vasanth Abantika Jena Chinmayee Dora Sujata Chakravarty Department of CSE Centurion University of Technology & Management Bhubaneswar Odisha India Department of ECE Centurion University of Technology & Management Bhubaneswar Odisha India

ISBN: (数字)9798350355093

ISBN: (纸本)9798350355109

Yoga pose detection is challenging in computer vision due to variations in body postures and environmental conditions. Recent advancements in DL models have demonstrated encouraging achievements in this field. This study integrates deep learning (DL) and Machine learning (ML) techniques to detect and monitor 20 Yoga postures through the real-time application. DL techniques like OpenPose, PoseNet, and PIFPAF are applied to the image and video dataset to obtain the keypoint features. These features are combined and provided to train various ML classifiers for Yoga posture detection tasks. Integrating AI augmentation technique Generative Adversarial Networks (GANs) plays a crucial role in improving the robustness and accuracy of the models. GANs are employed to generate synthetic data that mimics real-world variations in yoga poses and environments. By generating realistic variations in poses, backgrounds, lighting, and body shapes, GAN helped the models become more resilient to complex poses and diverse environmental conditions, enhancing their generalization capabilities. All the classifiers showed improvement with augmentation, whereas the Random Forest classifier performed the best in all parameters. Further, the model deployed with a webcam feed for estimating the Yoga pose by the yoga practitioner indicating accuracy level.

关键词： Accuracy Webcams Shape Computational modeling Pose estimation Streaming media Signal processing Generative adversarial networks real-time systems Synthetic data

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deep Neural Network (DNN) Based Synthetic Aperture Radar (SAR) Processor 14

Deep Neural Network (DNN) Based Synthetic Aperture Radar (SA...

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14th European Conference on Synthetic Aperture Radar, EUSAR 2022

作者： Ahmed, Usman Iqbal Rabus, Bernhard Haas, Jarrod BurnabyBCCA V5A 0E5 Canada

ISBN: (纸本)9783800758234

Synthetic Aperture Radar (SAR) is one of the main sources of remote sensing data today. SAR raw data focussing is complicated and time consuming, therefore, is mostly done offline with sophisticated algorithms. deep learning (DL) has widespread use in the SAR applications domain but processing radar / SAR raw data into processed outputs has rarely been targeted. We have explored the potential of deep Neural Networks (DNNs) to focus SAR raw data into Single Look Complex (SLC) images. Our method has potential for real-time processing of SAR data onboard airborne and spaceborne platforms. Promising results have been achieved for SAR raw data from the European Remote Sensing (ERS-1) satellite, originally acquired by the Alaska Satellite Facility (ASF). A Complex Valued DNN (CV-DNN) was designed and trained on two images of ~5700x27000 pixels over North America, which were broken down into smaller chips of 512x1024 pixels for training and validation purposes. The trained network was then able to focus raw data of a third image under test. A Structural Similarity Index (SSIM) of 0.749 with a mean squared error of 0.0029 was achieved for the output in comparison with the ground truth. Our research can serve as a steppingstone to exploit the unexplored potential of DNNs in the SAR focussing domain. © 2022 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Synthetic aperture radar

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Design and Implementation of a Double-Camera-Based Silkworm Cocoon Classification System with deep Vision 14th

Design and Implementation of a Double-Camera-Based Silkwo...

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14th International Conference on Computer Engineering and Networks, CENet 2024

作者： Yang, Chengjun Zhang, Yalei Lu, Xinxin Jia, Changbao Song, Huaning Hechi University YiZhou City China

ISBN: (纸本)9789819642281

The silkworm industry holds great potential for intelligence and automation. This study aims to enhance the intelligence of cocoon processing and increase economic benefits, exploring the application of deep vision technology in high-precision automated classification of silkworm cocoons. By deploying a dual-camera system, it captures the features of both sides of cocoon clusters simultaneously, improving the comprehensiveness and efficiency of detection. The study uses the Faster R-CNN algorithm for cocoon positioning and the Mask R-CNN algorithm for identifying diseased areas on the cocoons. First, Faster R-CNN is employed for object detection, followed by Mask R-CNN to generate precise masks of the diseased areas. These models are installed at the cameras on both sides of the conveyor belt to achieve real-time cocoon positioning and disease area identification. Experiments on a self-built cocoon dataset show that the object detection model achieved an accuracy of 94.9%, and the instance segmentation model achieved an average precision (mAP) of 92.9%. The developed system has significantly improved cocoon classification, providing key technical support for the intelligent production of the cocoon industry. It helps manage and protect cocoon resources more effectively, holding broad application prospects. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： image segmentation

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Location Identification and Personalized Recommendation of Tourist Attractions Based on image processing

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TRAITEMENT DU SIGNAL 2021年第1期38卷 197-205页

作者： Zhang, Qian Liu, Yi Liu, Lei Lu, Shuang Feng, Yuxue Yu, Xiao Zhengzhou Univ Light Ind Sch Art & Design Zhengzhou 450002 Peoples R China

Currently, tourists tend to plan travel routes and itineraries by searching for relevant information on tourist attractions via the Internet and intelligent terminals. However, it is difficult to achieve good retrieval effect on tourist attraction images with text labels. Based on deep learning, the visual location identification faces such defects as frequent mismatching, high probability of weak matching, and long execution time. To solve these defects, this paper puts forward a novel method for location identification and personalized recommendation of tourist attractions based on image processing. Specifically, the authors detailed the ideas and steps of the location identification algorithm for tourist attractions. The algorithm, grounded on hash retrieval, encompasses two stages: an offline stage, and an online stage. Besides, a personalized recommendation model for tourist attractions based on geographical location and time period. Finally, the proposed algorithm and model were proved accurate and effective through experiments.

关键词： image processing tourist attractions location identification personalized recommendation

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