Research and realization of video target detection system based on deep learning

Fan, Tao

doi:10.1142/s0219691319410108

Cited by 16 publications

(9 citation statements)

References 21 publications

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“…The region prediction method is actually a feature detection method for targets based on twostage, which consists of two sub-networks. One sub-network aims to predict the candidate region, and the other is responsible for analyzing and identifying the candidate region [8,9].…”

Section: Research Methods Based On Region Proposalmentioning

confidence: 99%

Application of local fully Convolutional Neural Network combined with YOLO v5 algorithm in small target detection of remote sensing image

Liu

et al. 2021

PLoS ONE

221

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This exploration primarily aims to jointly apply the local FCN (fully convolution neural network) and YOLO-v5 (You Only Look Once-v5) to the detection of small targets in remote sensing images. Firstly, the application effects of R-CNN (Region-Convolutional Neural Network), FRCN (Fast Region-Convolutional Neural Network), and R-FCN (Region-Based-Fully Convolutional Network) in image feature extraction are analyzed after introducing the relevant region proposal network. Secondly, YOLO-v5 algorithm is established on the basis of YOLO algorithm. Besides, the multi-scale anchor mechanism of Faster R-CNN is utilized to improve the detection ability of YOLO-v5 algorithm for small targets in the image in the process of image detection, and realize the high adaptability of YOLO-v5 algorithm to different sizes of images. Finally, the proposed detection method YOLO-v5 algorithm + R-FCN is compared with other algorithms in NWPU VHR-10 data set and Vaihingen data set. The experimental results show that the YOLO-v5 + R-FCN detection method has the optimal detection ability among many algorithms, especially for small targets in remote sensing images such as tennis courts, vehicles, and storage tanks. Moreover, the YOLO-v5 + R-FCN detection method can achieve high recall rates for different types of small targets. Furthermore, due to the deeper network architecture, the YOL v5 + R-FCN detection method has a stronger ability to extract the characteristics of image targets in the detection of remote sensing images. Meanwhile, it can achieve more accurate feature recognition and detection performance for the densely arranged target images in remote sensing images. This research can provide reference for the application of remote sensing technology in China, and promote the application of satellites for target detection tasks in related fields.

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Section: Research Methods Based On Region Proposalmentioning

confidence: 99%

Application of local fully Convolutional Neural Network combined with YOLO v5 algorithm in small target detection of remote sensing image

Liu

et al. 2021

PLoS ONE

221

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“…The development of deep learning has made more and more researchers pay attention to sewage treatment. Among them, Fan T studied the latest achievement of machine learning methods (deep learning) to achieve target category detection and proposed an improved meaning-based background extraction algorithm and a region of interest extraction algorithm for reducing image pixels [ 1 ]. Luo G studied infrared target detection technology, using image processing algorithms to automatically detect objects in chaotic backgrounds and strong noisy environments.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Target Detection System for Sewage Treatment

Lin

Wang

et al. 2022

Computational Intelligence and Neuroscience

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Object detection is to identify objects and then find some objects of interest. With the development of computers, target detection has evolved from traditional detection methods to artificial intelligence methods, and the latter are mainly based on some algorithms of deep learning. This paper mainly tests the treated sewage. First, the neural network and convolutional neural network algorithms in deep learning are studied, and then a target detection system is built based on these two algorithms. Finally, the treated sewage is detected and then compared with that of the traditional target detection system. The experimental results show that the target detection system of the convolutional neural network algorithm has a very stable recognition rate for the treated sewage, swinging around 70%, and the amplitude is not large. However, the target detection system of the neural network algorithm is not very stable in the recognition rate of the treated sewage, and the recognition rate is about 60%.

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“…In recent years, deep learning has made remarkable achievements in target detection, computer vision, natural language processing, automatic speech recognition, and semantic analysis (Fan, 2020). Compared with the shallow algorithm model of traditional machine learning, deep learning shows remarkable superiority in feature extraction and modeling.…”

Section: Autotun Methodsmentioning

confidence: 99%

Automatic Diagnosis of Soybean Leaf Disease by Transfer Learning

Gong¹,

Chen²,

Liu³

2022

American Journal of Biochemistry and Biotechnology

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Soybean diseases and insect pests are important factors that affect the output and quality of soybeans, thus it is necessary to do correct inspection and diagnosis of them. For this reason, based on improved transfer learning, this study proposed a classification method for soybean leaf diseases. Firstly, leaves were segmented from the whole image after removing the complicated background images. Secondly, the data-augmented method was applied to amplify the separated leaf disease image dataset to reduce overfitting. At last, the automatically fine-tuning convolutional neural network (Autotun) was adopted to classify the soybean leaf diseases. The verification accuracy of the proposed method is 94.23, 93.51 and 94.91% on VGG-16, ResNet-34 and DenseNet-121 networks respectively. Compared with the traditional finetuning method of transfer learning, the results show that this method is better than the traditional transfer learning method.

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Research and realization of video target detection system based on deep learning

Cited by 16 publications

References 21 publications

Application of local fully Convolutional Neural Network combined with YOLO v5 algorithm in small target detection of remote sensing image

Application of local fully Convolutional Neural Network combined with YOLO v5 algorithm in small target detection of remote sensing image

Deep Learning Target Detection System for Sewage Treatment

Automatic Diagnosis of Soybean Leaf Disease by Transfer Learning

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