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deep learning-based image processing framework for efficient surface litter detection in Computer Vision applications

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JOURNAL OF RADIATION RESEARCH AND APPLIED SCIENCES 2025年第2期18卷

作者： Shad, Ifza Zhang, Zuping Asim, Muhammad Al-Habib, Mohammed Chelloug, Samia Allaoua Abd El-Latif, Ahmed Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Prince Sultan Univ Coll Comp & Informat Sci EIAS Data Sci Lab Riyadh 11586 Saudi Arabia Prince Sultan Univ Ctr Excellence Quantum & Intelligent Comp Riyadh 11586 Saudi Arabia Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Technol POB 84428 Riyadh 11671 Saudi Arabia Menoufia Univ Fac Sci Dept Math & Comp Sci Shibin Al Kawm 32511 Egypt

Computer vision plays a vital role in automating environmental analysis by enabling real-time object detection and classification in diverse conditions. Litter pollution poses significant health and environmental risks due to inefficient disposal, and manual oversight is labor intensive. Effective litter detection is crucial for large-scale environmental monitoring. However, existing models face challenges such as the complexity of detecting shadowy objects in varying lighting conditions (e.g., during rain or under sun rays), difficulty in recognizing small objects, low accuracy, and poor real-time performance. Existing two-stage detectors, such as Faster R-CNN, also struggle with these issues. This paper introduces an automated deep learning-based image processing approach for accurate litter detection across different locations, using an enhanced version of YOLOv9s called LD-YOLOv9s. Key improvements in this novel approach include replacing convolutional layers with DynConvLayer in the backbone, integrating an SDConv-ADown module to substitute down-sampling layers in the neck, and using MPD-IoU instead of CIoU. These modifications reduce the chances of overlooking small objects, such as caps or lids, which had the least class meaning in the dataset, achieving a mAP of 78.3% with an inference time of 6.7ms. A significant contribution of this work is the LD-2024 dataset, curated from indoor and outdoor environments with manually annotated images. Performance comparisons were made with several YOLO versions (YOLOv3, YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLOv9 variants) and traditional object detectors (Faster R-CNN, Center Net, Retina Net, Cascade R-CNN). Ablation studies validate the effectiveness of LD-YOLOv9s, which outperforms conventional methods, achieving a 6.3% improvement in mean average precision (mAP) over YOLOv9s on the LD-2024 dataset.

关键词： deep learning Computer vision image processing Litter Detection YOLO DynConvLayer SDConv MPD-IoU LD-YOLOv9s Object Detection

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Lane detection using the concept of deep learning and Digital image processing

Lane detection using the concept of Deep learning and Digita...

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Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： Shashi Kant Ojha Abhishek Kumar Sandeep Kumar CSE Department Sharda University Greater Noida

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

Lane detection technology plays a pivotal role in enabling autonomous navigation in vehicles. However, existing systems primarily cater to well-structured roads with clear lane markings, rendering them ineffective in scenarios where markings are unclear or absent. This study critically evaluates an existing approach for detecting lanes on unmarked roads, followed by the proposal of an enhanced methodology. Both approaches leverage digital image processing techniques and rely solely on vision or camera data. The primary objective is to derive real-time curvature values to facilitate driver-assistance systems in making necessary turns and preventing vehicles from veering off-road.

关键词： deep learning Computers Lane detection Navigation Roads Digital images Rendering (computer graphics) Cameras real-time systems Proposals

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Automated bank cheque verification using image processing and deep learning methods

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MULtimeDIA TOOLS AND APPLICATIONS 2021年第4期80卷 5319-5350页

作者： Agrawal, Prateek Chaudhary, deepak Madaan, Vishu Zabrovskiy, Anatoliy Prodan, Radu Kimovski, Dragi Timmerer, Christian Univ Klagenfurt Klagenfurt Austria Lovely Profess Univ Phagwara India Petrozavodsk State Univ Petrozavodsk Russia Bitmovin Inc San Francisco CA USA

Automated bank cheque verification using image processing is an attempt to complement the present cheque truncation system, as well as to provide an alternate methodology for the processing of bank cheques with minimal human intervention. When it comes to the clearance of the bank cheques and monetary transactions, this should not only be reliable and robust but also save time which is one of the major factor for the countries having large population. In order to perform the task of cheque verification, we developed a tool which acquires the cheque leaflet key components, essential for the task of cheque clearance using image processing and deep learning methods. These components include the bank branch code, cheque number, legal as well as courtesy amount, account number, and signature patterns. our innovation aims at benefiting the banking system by re-innovating the other competent cheque-based monetary transaction system which requires automated system intervention. For this research, we used institute of development and research in banking technology (IDRBT) cheque dataset and deep learning based convolutional neural networks (CNN) which gave us an accuracy of 99.14% for handwritten numeric character recognition. It resulted in improved accuracy and precise assessment of the handwritten components of bank cheque. For machine printed script, we used MATLAB in-built OCR method and the accuracy achieved is satisfactory (97.7%) also for verification of Signature we have used Scale Invariant Feature Transform (SIFT) for extraction of features and Support Vector Machine (SVM) as classifier, the accuracy achieved for signature verification is 98.10%.

关键词： Cheque truncation system image segmentation Bank cheque clearance image feature extraction Convolution neural network Support vector machine Scale invariant feature transform

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Enhancing Palm Recognition Accuracy Through image processing deep learning Techniques

Enhancing Palm Recognition Accuracy Through Image Processing...

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Data Science and Network Security (ICDSNS), International Conference on

作者： J. Sheela Mercy S. Silvia Priscila Department of Computer Applications Bharath Institute of Higher Education and Research Chennai India Department of Computer Science Bharath Institute of Higher Education and Research Chennai India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

Palm recognition systems play an important role in biometric authentication; however, existing systems frequently have low accuracy and resiliency due to problems such as changing lighting conditions, occlusions, and hand orientations. The paper describes a novel way to improve palm recognition accuracy by combining modern image processing and deep learning approaches. Unlike traditional systems that utilize handcrafted features and shallow learning algorithms, the proposed system uses convolutional neural networks (CNNs) for feature extraction and classification, as well as advanced image preprocessing techniques. The technology preprocesses palm images to increase quality and consistency, thereby reducing the impact of environmental factors. Cross-validation trials yielded consistent results, with an average accuracy of 95%, precision of 92%, recall of 96%, and Fl-score of 94%. Furthermore, computational efficiency comparisons reveal that the proposed system beats existing ones in terms of training time, inference time, and model size, indicating that it is a promising option for accurate and efficient palm recognition in a variety of real-world applications.

关键词： Training deep learning Accuracy image recognition Computational modeling Lighting Biometric authentication

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TrashIntelAI- Garbage Collection Automation using deep learning and image processing

TrashIntelAI- Garbage Collection Automation using Deep Learn...

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Information Technologies and Communications (GCITC), Global Conference on

作者： Ranjana Jadhav Aditya Mhaske Aditya Nawale Mokshit Oswal Sumit Mulhar Dept. of Artificial Intelligence and Data Science Vishwakarma Institute of Technology Pune India

Garbage collection in urban areas has become a major challenge due to the increase in trash production. New technologies, including the application of deep learning and image processing methods, have been created to solve this problem. In this study, we present an automated garbage collection system that recognizes and categorizes rubbish in real-time using computer vision and machine learning methods. A deep learning model classifies the garbage into several categories, and the system also contains an image processing pipeline that pre-processes incoming photos and extracts relevant data. We executed experiments on a real-world dataset to assess the performance of the suggested solution. The outcomes demonstrate that the suggested approach classifies garbage with a high degree of accuracy. The system can recognize and categorize various waste categories, including recyclables, organic garbage, and non-recyclables, which can then be collected individually for proper disposal. This is made possible by the application of deep learning and image processing algorithms. The suggested approach can potentially increase trash disposal efficiency, reduce waste sent to landfills, and promote a cleaner and healthier environment in metropolitan settings. The burden of garbage collection staff can be reduced, and the danger of harm can be reduced, with real-time monitoring and automated waste collection.

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Determination of License Plate Using deep learning and image processing 6th

Determination of License Plate Using Deep Learning and Image...

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6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Tyagi, S.S. Rathore, Riti Rathore, Vaibhav Rai, Shruti Rohila, Shruti Ajay Kumar Garg Engineering College Ghaziabad India

ISBN: (纸本)9789819933143

Automatic license plate recognition (ALPR) provides a way to extract the vehicle’s number plates using the computer vision technology. To achieve higher accuracy rates, ALPR is widely used across the world in traffic controlling, automated toll collected system and in society parking, etc. Any ALPR system consists of following processes, viz. video recording as an input, identifying that object is moving or not, differentiating between vehicles and non-vehicles, localization of license plate of vehicles, extractions of characters of license plate, and recognition of segmented characters. In the real world, due to changing weather conditions, polluted environments, different intensities of headlight, etc., it is very difficult to capture an input image. The ALPR processes become more difficult if the license plates of each vehicle do not follow the rules and protocols set by the corresponding Motor Vehicles Department. If the license plate is broken, tilted, dirty, or missing some characters, then all these difficulties add up to generate an efficient ALPR system. Traditional LPR methods extract characters by one-by-one characters segmentation which is very time consuming, but our method provides image preprocessing, machine learning, and neural network techniques which makes it time saving and achieves greater accuracy rates. Neural network technique consists of image preprocessing, contour extraction, rotation, distortion, and character segmentation processes. Experimentally, we can verify our proposed method on a public dataset, and the results state that our method overcomes the drawbacks of previous methods and it achieves accuracies of more than 97% for different types of license plate datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Video recording

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A mechatronics data collection, image processing, and deep learning platform for clinical posture analysis: a technical note

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PHYSICAL AND ENGINEERING SCIENCES IN MEDICINE 2021年第3期44卷 901-910页

作者： Salahzadeh, Zahra Rezaei-Hachesu, Peyman Gheibi, Yousef Aghamali, Ali Pakzad, Hamed Foladlou, Saeideh Samad-Soltani, Taha Tabriz Univ Med Sci Fac Rehabil Physiotherapy Dept 29 Bahman St Tabriz Iran Tabriz Univ Med Sci Sch Management & Med Informat Dept Hlth Informat Technol Tabriz Iran Univ Tabriz Fac Comp Engn Dept Artificial Intelligence Tabriz Iran Sanam Sahand Hlth Promot Ind Dept Res & Dev Tabriz Iran Islamic Azad Univ Tabriz Dept Biomed Engn Tabriz Iran

Static and dynamic posture analysis was a critical clinical examination in physiotherapy and rehabilitation. It was a time-consuming task for clinicians, so a semi-automatic method can facilitate this process as well as provide well-documented medical records and strong infrastructure for deep learning scenarios. The current research presents a mechatronics platform for static and real-time dynamic posture analysis, which consisted of hybrid computational modules. Our study was a developmental and applied research according to a system development life cycle. The designed modules are as follows: (1) a mechanical structure includes patient place, 360-degree engine, mirror, laser, distance meter, and cams;(2) a software module includes data collection, electronic medical record, semi-automatic image analysis, annotation, and reporting, and (3) a network to exchange raw data with deep learning server. Patients were informed about the research by their healthcare provider and all data were transformed into a Fourier format, in which the patients remained autonomous without a bit of information. The results show acceptable reliability and validity of the instruments. Also, a telerehabilitation application was designed to cover the patients after diagnosis. We suggest a longer time for data acquisition. It will lead to a more accurate and fully automated dynamic posture analysis. The result of this study suggest that the designed mechatronics device used in conjunction with smartphone application is a valid tool that can be used to obtain reliable measurements.

关键词： Posture image processing Telerehabilitation Decision support system

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A two-stage deep transfer learning model and its application for medical image processing in Traditional Chinese Medicine

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KNOWLEDGE-BASED SYSTEMS 2022年 239卷 108060-108060页

作者： Zhang, Xu Chen, Zhikui Gao, Jing Huang, Wei Li, Peng Zhang, Jianing Dalian Univ Technol Sch Software Technol Dalian Peoples R China Key Lab Ubiquitous Network & Serv Software Liaoni Dalian Peoples R China Dalian Med Univ Dept Crit Care Med Affiliated Hosp 1 Dalian Peoples R China

People are deepening the study on model explainability as well as performance to better understand models' decisions from the human perspective. However, the lack of rare clinical diagnosis data always limits the power of the emerging data-driven deep diagnosis methods, and the traditional deep transfer learning (DTL) applicable for the case is often insufficient to learn specific features in medical image processing, leading to poor explainability. To address those challenges, a two-stage deep transfer learning model is proposed and applied to assist in the Traditional Chinese Medicine (TCM) tongue diagnosis. Especially, a two-stage transfer learning training strategy is designed to loose the data dependence of deep learning on the domain data, which is composed of the imitate stage that discovers shared basic source features and the transfer stage to relearn target patterns, with good explainability. The corresponding deep squeeze-and-excitation convolutional network is proposed to learn the clinical patterns of tongue symptoms, in which a three-layer feature pyramid network fuses the multi-scale tongue features. Extensive experiments are conducted on the real clinical dataset in terms of classification accuracy and learning efficiency. The resulting accuracy of the proposed model proves its performance advantage with the recognition time achieving real-time performance. (c) 2021 Elsevier B.V. All rights reserved.

关键词： deep transfer learning deep neural networks Medical image processing Traditional Chinese Medicine Tongue diagnosis

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A Survey on Multi-Classification of Pulmonary Diseases Using image processing and deep learning Algorithms

A Survey on Multi-Classification of Pulmonary Diseases Using...

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Emerging Techniques in Computational Intelligence (ICETCI), International Conference on

作者： Uma Maheswari. V Niveditha Reddy Mandala Roopika Ponnur Computer Science and Engineering Chaitanya Bharathi Institute of Technology Hyderabad India

ISBN: (数字)9798350387063

ISBN: (纸本)9798350387070

Multi-classification of pulmonary diseases poses a significant challenge, particularly when diseases share similar radiological presentations like lung cancer, pneumonia, and COVID-19. While chest CT scan images are essential for diagnosis, manual interpretation by radiologists is time-consuming and difficult, even for experts. deep learning algorithms offer promise in this domain, demonstrating high accuracy in classifying these diseases from CT images. This review examines recent advances in using deep learning for multiclassification of pulmonary diseases. It discusses various preprocessing techniques, deep learning architectures to enhance model performance. Additionally, a comparative analysis of different deep learning models on publicly available CT datasets is presented. The findings suggest that deep learning models could serve as valuable tools for multiclassification of pulmonary diseases, potentially improving diagnostic accuracy and efficiency. Nonetheless, further research is required to develop and validate these models on large, diverse datasets and ensure their robustness in real-world clinical settings.

关键词： deep learning Surveys COVID-19 image segmentation Accuracy Pneumonia Computed tomography image edge detection Lung cancer Classification algorithms

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Human Action Recognition (HAR) using image processing on deep learning 13

Human Action Recognition (HAR) using Image Processing on Dee...

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13th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2023

作者： Ismail, Ahmad Puad Azahar, Muhammad Afiq Bin Tahir, Nooritawati Md Daud, Kamarulazhar Kasim, Nazirah Mohamat Universiti Teknologi Mara Cawangan Pulau Pinang Electrical Engineering Studies Malaysia Universiti Teknologi Mara Electrical Engineering Studies Shah Alam Malaysia

ISBN: (纸本)9798350323184

The advancement of artificial intelligence (AI) has bought many advances to human society as a whole. By using daily activities and integrating the technology from the fruits of AI, we can manage to gain further access to knowledge we can only begin to imagine. In identifying human action recognition (HAR);processing photos and videos to discern whether a human is present, then mapping the subject classified, which lastly determines the action being carried out is the objective. To achieve this, various steps are taken and careful approach is required, with the extensive amount of research, numerous troubleshooting and experimentation is required. The AI architecture has to learn from dataset collected for it to discern the identification of action properly. HAR is achieved by using Python code using real-time webcam feed. Human pose detection library known as MediaPipe Pose Detection detects human anatomy from input through joints key-points. MediaPipe algorithm that extract features in x-y-z axis with visibility (four variables) and the extracted data is trained using CNN-LSTM based on the trained and tested algorithm classifier model. The output obtained produced an RGB-skeleton and an action label on the detected subject as standing, waving, walking and sitting, has yielded good results. © 2023 IEEE.

关键词： image processing

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