Deep learning-based parking occupancy detection framework using ResNet and VGG-16

Thakur, Narina; Bhattacharjee, Eshanika; Jain, Rachna; Acharya, Biswaranjan; Hu, Yu-Chen

doi:10.1007/s11042-023-15654-w

Cited by 16 publications

(4 citation statements)

References 29 publications

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“…This efficiency makes it particularly suitable for real-time applications such as license plate detection in intelligent transportation systems. (23) The research journey initiated in this study goes beyond simple algorithm implementation and includes careful optimization and strategic deployment steps. Here, the system undergoes extensive tuning by carefully tuning hyperparameters such as the number of regions provided, input image size, and object type.…”

Section: Methodsmentioning

confidence: 99%

Real-time number plate detection using AI and ML

Swathi,

Sai Tejaswi,

Amanulla Khan

et al. 2024

Gamification and Augmented Reality

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The abstract presents a research study focusing on real-time license plate verification, a key feature of electronic systems that operate by rapidly identifying and removing identification numbers from vehicle registration in a dynamic global environment. The research leverages the combination of artificial intelligence (AI) and machine learning (ML) techniques, specifically the integration of region-based convolutional neural networks (RCNN) and advanced RCNN algorithms, to create a powerful and readily available system. In terms of methods, this research optimizes algorithm performance and deploys the system in a cloud-based environment to improve accessibility and scalability. Through careful design and optimization, the proposed system has achieved a consistent result in license recognition, as evident from the well-accounted evaluation of performance, including precision, recall, and computational efficiency. The results demonstrate the efficiency and usability of this system in a real installation and promise to revolutionize automatic vehicle identification. Finally, the integration of artificial intelligence and machine learning technology into real-time license plate recognition signifies changes in traffic management, assessment safety and smart city plans. Therefore, interdisciplinary collaboration and continuous innovation are crucial to shaping a sustainable and balanced future for intelligent transportation systems.

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Section: Methodsmentioning

confidence: 99%

Real-time number plate detection using AI and ML

Swathi,

Sai Tejaswi,

Amanulla Khan

et al. 2024

Gamification and Augmented Reality

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“…They also established the CNRPARK-EXT dataset to validate their model. Thakur et al [21] employed CNN for image category recognition tasks and used ResNet50 and VGG16 models for parking space detection and classification. Wang et al [22] introduced a transformer-based Global Perception Feature Extractor (GPFE) module to achieve global awareness and enhance detection accuracy and robustness.…”

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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“…The literature review provides a comprehensive examination of the existing body of knowledge concerning the utilization of AI and CV technologies in transportation systems and parking facility management, with a specific focus on their application in lane identification and parking spot detection. Several studies [17,18] have extensively explored the integration of deep learning and transfer learning techniques to enhance lane identification and parking spot detection across various contexts. These investigations [17][18][19][20] have effectively demonstrated the prowess of deep learning models, notably convolutional neural networks (CNN), in proficiently analyzing video data and accurately detecting lanes and parking spaces.…”

Section: Related Workmentioning

confidence: 99%

“…Several studies [17,18] have extensively explored the integration of deep learning and transfer learning techniques to enhance lane identification and parking spot detection across various contexts. These investigations [17][18][19][20] have effectively demonstrated the prowess of deep learning models, notably convolutional neural networks (CNN), in proficiently analyzing video data and accurately detecting lanes and parking spaces.…”

Section: Related Workmentioning

confidence: 99%

AI-Powered Intelligent Seaport Mobility: Enhancing Container Drayage Efficiency through Computer Vision and Deep Learning

Lee,

Chatterjee,

Cho

2023

Applied Sciences

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The rapid urbanization phenomenon has introduced multifaceted challenges across various domains, including housing, transportation, education, health, and the economy. This necessitates a significant transformation of seaport operations in order to optimize smart mobility and facilitate the evolution of intelligent cities. This conceptual paper presents a novel mathematical framework rooted in deep learning techniques. Our innovative model accurately identifies parking spaces and lanes in seaport environments based on crane positions, utilizing live Closed-Circuit Television (CCTV) camera data for real-time monitoring and efficient parking space allocation. Through a comprehensive literature review, we explore the advantages of merging artificial intelligence (AI) and computer vision (CV) technologies in parking facility management. Our framework focuses on enhancing container drayage efficiency within seaports, emphasizing improved traffic management, optimizing parking space allocation, and streamlining container movement. The insights from our study provide a foundation that could have potential implications for real-world applications. By integrating cutting-edge technologies, our proposed framework not only enhances the efficiency of seaport operations, but also lays the foundation for sustainable and intelligent seaport systems. It signifies a significant leap toward the realization of intelligent seaport operations, contributing profoundly to the advancement of urban logistics and transportation networks. Future research endeavors will concentrate on the practical implementation and validation of this pioneering mathematical framework in real-world seaport environments. Additionally, our work emphasizes the crucial need to explore further applications of AI and CV technologies in seaport logistics, adapting the framework to address the evolving urbanization and transportation challenges. These efforts will foster continuous advancements in the field, shaping the future of intelligent seaport operations.

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Deep learning-based parking occupancy detection framework using ResNet and VGG-16

Cited by 16 publications

References 29 publications

Real-time number plate detection using AI and ML

Real-time number plate detection using AI and ML

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

AI-Powered Intelligent Seaport Mobility: Enhancing Container Drayage Efficiency through Computer Vision and Deep Learning

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