Analyzing parking lots vacancy detection algorithms using Mask R-CNN implementations

Vilceanu, Razvan; Onița, Mihai; Ternauciuc, Andrei

doi:10.1109/isetc50328.2020.9301141

Cited by 8 publications

(2 citation statements)

References 17 publications

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“…Chest X-rays reveal areas of opacity or consolidation, indicative of lung inflammation. Prompt diagnosis and treatment with antibiotics or antiviral medications are crucial to prevent complications [24]. Chest pneumonia can be severe, particularly in vulnerable populations, and may necessitate hospitalization for respiratory support and monitoring.…”

Section: Methodsmentioning

confidence: 99%

Decision Making Systems for Pneumonia Detection using Deep Learning on X-Ray Images

Kozhamkulova,

Nurlybaeva,

Suleimenova

et al. 2024

ijacsa

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This research paper investigates the application of Convolutional Neural Networks (CNNs) for the classification of pneumonia using chest X-ray images. Through rigorous experimentation and data analysis, the study demonstrates the model's impressive learning capabilities, achieving a notable accuracy of 96% in pneumonia classification. The consistent decrease in training and validation losses across 25 learning epochs underscores the model's adaptability and proficiency. However, the research also highlights the challenge of dataset imbalance and the need for improved model interpretability. These findings emphasize the potential of deep learning models in enhancing pneumonia diagnosis but also underscore the importance of addressing existing limitations. The study calls for future research to explore techniques for addressing dataset imbalances, enhance model interpretability, and extend the scope to address nuanced diagnostic challenges within the field of pneumonia classification. Ultimately, this research contributes to the advancement of medical image analysis and the potential for deep learning models to aid in early and accurate pneumonia diagnosis, thereby improving patient care and clinical outcomes.

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Section: Methodsmentioning

confidence: 99%

Decision Making Systems for Pneumonia Detection using Deep Learning on X-Ray Images

Kozhamkulova,

Nurlybaeva,

Suleimenova

et al. 2024

ijacsa

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“…Their approach employed the YOLOv3 algorithm, Intersection over Union (IOU) methods, and overlap analysis, enabling real-time and accurate detection and classification of parked vehicles. Vilceanu et al [26] compared the advantages, disadvantages, and limitations of automatic and manual methods based on the Mask R-CNN architecture.…”

Section: Related Workmentioning

confidence: 99%

Research on parking space detection algorithm in complex environments based on improved YOLOv7

Wang,

Zhang,

Zhang

et al. 2023

Meas. Sci. Technol.

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In the context of complex parking environments, vehicle parking space detection faces challenges such as multi-scale, multi-angle, and occlusion issues, leading to low detection efficiency and problems with false positives and false negatives. In this study, we propose an improved vehicle parking space detection algorithm based on YOLOv7. Firstly, we enhance the convolutional layers by introducing the Mish activation function, thereby improving the model’s feature extraction capabilities and its ability to represent objects effectively. Secondly, we combine the parameter-free attention mechanism SimAM with feature pyramid modules and feature extraction modules to replace certain convolutional layers, thereby enhancing the reinforcement of critical parking space information and adaptability to variations in target scale. Finally, we replace the nearest-neighbor interpolation in the upsampling section with the lightweight operator CARAFE, effectively extracting parking space feature information and enhancing the algorithm’s feature fusion capabilities. Through ablation experiments and comparative trials on publicly available parking space datasets, our improved YOLOv7 algorithm achieves an mAP of 78.7%. Compared to the original algorithm, it demonstrates a 1.5% improvement in detection performance and a 5.6% increase in recall rate. These enhancements significantly improve parking space detection in complex environments, addressing issues such as false positives and false negatives, thereby meeting the performance requirements of parking space detection in parking lots.

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A Hierarchical Algorithm for In-city Parking Allocation Based on Open Street Map and AnyLogic Software

et al. 2023

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Analyzing parking lots vacancy detection algorithms using Mask R-CNN implementations

Cited by 8 publications

References 17 publications

Decision Making Systems for Pneumonia Detection using Deep Learning on X-Ray Images

Decision Making Systems for Pneumonia Detection using Deep Learning on X-Ray Images

Research on parking space detection algorithm in complex environments based on improved YOLOv7

A Hierarchical Algorithm for In-city Parking Allocation Based on Open Street Map and AnyLogic Software

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