EPSDNet: Efficient Campus Parking Space Detection via Convolutional Neural Networks and Vehicle Image Recognition for Intelligent Human–Computer Interactions

An, Qing; Wang, Haojun; Chen, Xijiang

doi:10.3390/s22249835

Cited by 5 publications

(4 citation statements)

References 39 publications

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“…Figure 4 shows the test the suggested approach for general use by applying it to photos of various automobiles. Training sets consisted of 6387 photos of automobiles, whereas validation sets consisted of 1146 images (see [21] for details). The 7533 photos were labelled by hand using tool, and then the resulting footnote files were transformed to TensorFlow's standard TFRecord data format using a Python script.…”

Section: Data Set Productionmentioning

confidence: 99%

AOA based Masked Region-CNN model for Detection of Parking Space in IoT Environment

et al. 2024

Int. Res. J. multidiscip. Technovation

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Uneven illumination has a significant impact on vision-based automatic parking systems, making it impossible to conduct a correct assessment of parking places in the presence of complicated picture data. In to address this issue, this work provides a deep learning-based system for visual recognition of parking spaces and picture processing. Artificial intelligence (AI) approaches can be used to identify a less expensive and easier-to-implement solution to the parking spot identification challenge, especially since the discipline of deep learning is reshaping the world. Using deep learning techniques, this study offers a dynamic, straightforward, and cost-effective algorithm for the detection of parking spots. In order to determine which parking spots are available and which are occupied, this method employs a Masked Region Based Convolutional Neural Network (MR-CNN) and the intersection over union approach. Cars in the training dataset were spaced more apart than those actually seen, which increased the accuracy of the identification between cars and parking spots. The AOA mechanism enhances the model's ability to focus on relevant regions within an image, improving accuracy in detecting parking spaces. This leads to precise identification of parking slots, reducing false positives and negatives. The sequence and quantity of parking spots, as well as the capacity to predict empty spots, were tested in a case study and found to be accurate. In the experimental results as the AOA based MR-CNN model stretched the accuracy as 98.50 and the recall value as 40.59 then the precision as 96.34 F1-measure as 57.95 correspondingly.

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Section: Data Set Productionmentioning

confidence: 99%

AOA based Masked Region-CNN model for Detection of Parking Space in IoT Environment

et al. 2024

Int. Res. J. multidiscip. Technovation

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“…Convolutional neural networks (CNNs) have been used extensively in a wide range of applications, including image recognition [ 48 , 49 ], brain–computer interfaces [ 50 , 51 ], natural language processing [ 52 , 53 ], and medical image analysis [ 54 , 55 , 56 , 57 ].…”

Section: Processing Us Scans Of Ivcmentioning

confidence: 99%

Processing Ultrasound Scans of the Inferior Vena Cava: Techniques and Applications

Policastro,

Mesin

2023

Bioengineering

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The inferior vena cava (IVC) is the largest vein in the body. It returns deoxygenated blood to the heart from the tissues placed under the diaphragm. The size and dynamics of the IVC depend on the blood volume and right atrial pressure, which are important indicators of a patient’s hydration and reflect possible pathological conditions. Ultrasound (US) assessment of the IVC is a promising technique for evaluating these conditions, because it is fast, non-invasive, inexpensive, and without side effects. However, the standard M-mode approach for measuring IVC diameter is prone to errors due to the vein movements during respiration. B-mode US produces two-dimensional images that better capture the IVC shape and size. In this review, we discuss the pros and cons of current IVC segmentation techniques for B-mode longitudinal and transverse views. We also explored several scenarios where automated IVC segmentation could improve medical diagnosis and prognosis.

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“…They are typically installed above each parking space and can detect changes in the infrared light caused by the presence of a vehicle. Video cameras [ 6 , 7 , 8 , 9 , 10 , 11 ] can be used to detect the presence or absence of a vehicle in a parking space. They are typically installed above each parking space and use image analysis software to detect changes in the video feed caused by the presence of a vehicle.…”

Section: Introductionmentioning

confidence: 99%

Rethinking Feature Generalization in Vacant Space Detection

Manh

2023

Sensors

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Vacant space detection is critical in modern parking lots. However, deploying a detection model as a service is not an easy task. As the camera in a new parking is set up at different heights or viewing angles from the original parking lot where the training data are collected, the performance of the vacant space detector could be degraded. Therefore, in this paper, we proposed a method to learn generalized features so that the detector can work better in different environments. In detail, the features are suitable for a vacant detection task and robust to environmental change. We use a reparameterization process to model the variance from the environment. In addition, a variational information bottleneck is used to ensure the learned feature focus on only the appearance of a car in a specific parking space. Experimental results show that performances on a new parking lot increase significantly when only data from source parking are used in the training phase.

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EPSDNet: Efficient Campus Parking Space Detection via Convolutional Neural Networks and Vehicle Image Recognition for Intelligent Human–Computer Interactions

Cited by 5 publications

References 39 publications

AOA based Masked Region-CNN model for Detection of Parking Space in IoT Environment

AOA based Masked Region-CNN model for Detection of Parking Space in IoT Environment

Processing Ultrasound Scans of the Inferior Vena Cava: Techniques and Applications

Rethinking Feature Generalization in Vacant Space Detection

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