Machine learning-driven pedestrian detection and classification for electric vehicles: integrating Bayesian component network analysis and reinforcement region-based convolutional neural networks

Devipriya, A; Prabakar, D.; Singh, Laxman; Oliver, Avital; Qamar, Shamimul; Azeem, Abdul

doi:10.1007/s11760-023-02681-1

Cited by 3 publications

(1 citation statement)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If there is a pedestrian, the bounding box is employed to label the specific location of the pedestrian and give the confidence score [6,9]. With the vigorous advancement of artificial intelligence technology, pedestrian detection has shown a broad spectrum of application scenarios in the arena of automatic driving, human-computer interaction, intelligent video surveillance, and urban street view [12,29]. In the early, pedestrian detection methods for manual feature extraction.…”

Section: Pedestrian Detectionmentioning

confidence: 99%

YOLOv7-PD: Incorporating DE-ELAN and NWD-CIoU for Advanced Pedestrian Detection Method

He,

Wan

2024

ITC

View full text Add to dashboard Cite

In the pedestrian detection task, the excessive depth of the convolutional network in YOLOv7 results in an abundance of background feature information, thereby posing challenges for the model to accurately locate and detect pedestrians, particularly in small-scale or heavily occluded scenarios. To handle this problem, we propose a pedestrian detection model called YOLOv7-PD, to strengthen the accuracy of detecting small-scale pedestrians and occluded pedestrians. First of all, we propose an improved module called DE-ELAN, an improvement on the existing E-ELAN module, which is based on Omni-Dimensional Dynamic Convolution (ODConv). This module leverages four complementary attention types to enhance feature extraction, capturing rich contextual information. Then, we propose a lightweight receptive field enhancement module called light-REFM, which constructs a pyramid structure and acquires fine-grained multi-scale information through dilated convolutions of different sizes. Finally, we propose an improved regression loss function based on the Normalized Wasserstein Distance (NWD) that combines NWD with Complete-IoU (CIoU), enabling precise position and feature capture for small targets. On the Citypersons dataset, YOLOv7-PD outperforms YOLOv7, improving the average precision (AP) by 7% and reducing the miss rate by 2.58%. Experiments on three challenging pedestrian detection datasets demonstrate a balance between precision and speed, achieving excellent performance.

show abstract

Section: Pedestrian Detectionmentioning

confidence: 99%

YOLOv7-PD: Incorporating DE-ELAN and NWD-CIoU for Advanced Pedestrian Detection Method

He,

Wan

2024

ITC

View full text Add to dashboard Cite

show abstract

Bff: Bi-stream feature fusion for object detection in hazy environment

Singh,

Parihar

2024

SIViP

View full text Add to dashboard Cite

Internal Combustion Engine Fault Detection Based on Random Convolutional Neural Networks

Zhang,

Shi

2024

J CIRCUIT SYST COMP

View full text Add to dashboard Cite

The internal combustion engine plays a very important role in many fields, and when the internal combustion engine fails, if it is not found in time, it may cause continuous damage to the internal combustion engine, and further affect the life of the entire mechanical system. To solve the above problems, this paper proposes fault detection of internal combustion engines based on a random convolutional neural network. The cylinder burst noise signal of the internal combustion engine is decomposed by a stationary wavelet, and the inclusion matrix is composed of partial decomposition coefficients. Using singular value theory, the singular value containing matrix is extracted as the characteristic of cylinder burst noise signal. The singular value is used as the input of a random convolutional neural network to train and identify faults. The AdaBound optimizer was then applied to adapt the learning rate to changes, thereby accelerating the weight update of the model. At the same time, the neurons in the structure are randomly deactivated by dropout technology to prevent complex collaborative responses to the training data, and the diagnosis results of each network model are integrated by Dempster synthesis rules. The experimental results show that the minimum mean square error of 0.00165 is achieved when there are 15 neurons in the hidden layer, and it gradually increases as the number of neurons increases. Therefore, it is determined that there should be 15 neurons in the hidden layer, and the trainLM algorithm is used. The decision factors for training, validation, cross-validation, and overall data are 0.98947, 0.98597, 0.97738, and 0.9801, respectively, all reaching the control accuracy of 0.95, which indicates that the random convolutional neural network proposed has a high accuracy for internal combustion engine fault detection. For the main bearing Y-directional force, as the gap increases, its fluctuation trend strengthens; the main bearing force changes within a small range at first, then increases sharply when the gap reaches 0.2[Formula: see text]mm, and some main bearings even experience significant impact loads.

show abstract

Machine learning-driven pedestrian detection and classification for electric vehicles: integrating Bayesian component network analysis and reinforcement region-based convolutional neural networks

Cited by 3 publications

References 25 publications

YOLOv7-PD: Incorporating DE-ELAN and NWD-CIoU for Advanced Pedestrian Detection Method

YOLOv7-PD: Incorporating DE-ELAN and NWD-CIoU for Advanced Pedestrian Detection Method

Bff: Bi-stream feature fusion for object detection in hazy environment

Internal Combustion Engine Fault Detection Based on Random Convolutional Neural Networks

Contact Info

Product

Resources

About