Vehicle Detection in Remote Sensing Image Based on Machine Vision

Zhou, Liming; Zheng, Chen; Yan, Huijie; Zuo, Xianyu; Qiao, Baojun; Fan, Minghu; Liu, Yang

doi:10.1155/2021/8683226

Cited by 7 publications

(4 citation statements)

References 38 publications

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“…The upper and lower channels are trained in parallel by 2 GPUs at the same time. The main technical content is: the image enters the input layer, goes through the continuous “convolution-pooling” process, then to the fully connected layer, and finally outputs the classification result [ 19 ].…”

Section: Methods Of Machine Vision and Intelligent Algorithms Based O...mentioning

confidence: 99%

Machine Vision and Intelligent Algorithm Based on Neural Network

Sun

2022

Computational Intelligence and Neuroscience

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Neural network algorithms and intelligent algorithms are hot topics in the field of deep learning. In this study, the neural network algorithm and intelligence are optimized, and it is used in simulation experiments to improve the target image recognition ability of the algorithm in the machine vision environment. First, this paper introduces the application of neural networks in the field of machine vision. Second, in the experiment, the improved VGG-16 convolutional neural network (CNN) model is applied to metal block defect detection. Experimental results show that the optimized network can classify metal block defects with the maximum accuracy of 99.28%. Then, the intelligent algorithm based on neural network is studied, and the CIFAR-10 data set is taken as the experimental target for training test and verification test. Using BP algorithm, particle swarm optimization algorithm (PSO-BP), and improved neural network algorithm, respectively, the convergence speed of ICS algorithm based on BP neural network is compared. In contrast, ICS-BP algorithm has the fastest convergence speed and converges when the number of iterations is 32, followed by PSO-BP algorithm.

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Section: Methods Of Machine Vision and Intelligent Algorithms Based O...mentioning

confidence: 99%

Machine Vision and Intelligent Algorithm Based on Neural Network

Sun

2022

Computational Intelligence and Neuroscience

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“…Lastly, to realize front-vehicle recognition, a feature-point clustering approach related to integrating temporal and spatial features was implemented. Zhou et al [17] projected a target detection technique called IR-PANet for the RSI of an automobile. SPP was utilized for strengthening the learning content in the backbone network CSPDarknet53.…”

Section: Related Workmentioning

confidence: 99%

“…In which, w_i represents the weighted matrix and b_i indicates the bias vector. The resultant of input gate i defines if the candidate value C created by novel input is along with cell state or not and it can be formulated as: 𝐶 ̃((𝑡)) = 𝑡𝑎𝑛ℎ(𝑤_𝑐 [ℎ^((𝑡 − 1) ), 𝑥^((𝑡) ) ] + 𝑏_𝑐 ) (17) whereas b_c and w_c denotes the equivalent bias vector and weight matrix. The cell state of present moment step c^((t)) was computed dependent upon the cell state of the preceding time step and candidate values of present time steps.…”

Section: 𝑓^((𝑡) ) = 𝜑(𝑤_𝑓 [ℎ^((𝑡 − 1) ) 𝑥^((𝑡) ) ] + 𝑏_𝑓 ) (15)mentioning

confidence: 99%

Vehicle Detection and Classification using Optimal Deep Learning on High-Resolution Remote Sensing Imagery for Urban Traffic Monitoring

Alotaibi,

Nagappan,

et al. 2023

Preprint

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Remote sensing images (RSI), such as aerial or satellite images, produce a large-scale view of the Earth's surface, getting them used to track and monitor vehicles from several settings like border control, disaster response, and urban traffic surveillance. Vehicle detection and classification utilizing RSIs is a vital application of computer vision and image processing. It contains locating and identifying vehicles from the image. It is done using many approaches having object detection approaches, namely YOLO, Faster R-CNN, or SSD, that utilize deep learning (DL) for locating and identifying the image. Also, the vehicle classification from RSIs contains classifying them dependent upon their variety, like trucks, motorcycles, cars, or buses utilizing machine learning (ML) techniques. This article designed and develop an automated vehicle type detection and classification using a chaotic equilibrium optimization algorithm with deep learning (VDTC-CEOADL) on high resolution RSIs. The presented VDTC-CEOADL technique examines the high-quality RSIs for the accurate detection and classification of vehicles. The VDTC-CEOADL technique employs a YOLO-HR object detector with Residual Network as a backbone model to accomplish this. In addition, CEOA based hyperparameter optimizer is de-signed for the parameter tuning of the ResNet model. For the vehicle classification process, the VDTC-CEOADL technique exploits the attention based long short term memory (ALSTM) mod-el. The performance validation of the VDTC-CEOADL technique is validated on high resolution RSI dataset, and the results portrayed the supremacy of the VDTC-CEOADL technique in terms of different measures.

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“…The regressionbased technique called as CFC-Net [8] is used for producing the feasible classification process. A feature-fusion related framework [9] is used to solve the issue of multi scale feature strategy. The semantic representations are combined with the shallow layers to identify the feature maps of layers through low-level data which utilizes to perform the object detection in multiple scales.…”

Section: Related Workmentioning

confidence: 99%

Data Aggregation Based Hybrid Deep Learning Technique for Identifying the Uncertainties and Accurate Object Detection

Patibandla¹,

Archana²,

Tanguturi³

2022

INDJCSE

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The deep learning concept for performing object detection plans to minimize the labeling cost by identifying the samples which increase the detection into the unlabeled pool. According to the object detection than the classification process as the designing and selection of procedure is very essential. The related works have been implemented the aggregation data process of several outputs and batch box process for improving the performance evaluation. The mean Average Precision 𝒎𝑨𝑷 is the main performance metric for identifying the accuracy of the object detection. The class imbalance problem has been solved using the background class in every group of sample images. The loss related weight algorithm for training group is proposed in this paper utilizing the batch boxes, aggregating data and also the 𝒎𝑨𝑷 enhancements are addressed to solve the class imbalance problem. Additionally, a sampling process is used for identifying the uncertainty and enhancing the object detection process. The performance results illustrate that the proposed framework generates good performance than the relevant technique and it will be used for realtime applications in an efficient manner.

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Vehicle Detection in Remote Sensing Image Based on Machine Vision

Cited by 7 publications

References 38 publications

Machine Vision and Intelligent Algorithm Based on Neural Network

Machine Vision and Intelligent Algorithm Based on Neural Network

Vehicle Detection and Classification using Optimal Deep Learning on High-Resolution Remote Sensing Imagery for Urban Traffic Monitoring

Data Aggregation Based Hybrid Deep Learning Technique for Identifying the Uncertainties and Accurate Object Detection

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