A Novel Image Recognition Method Based on DenseNet and DPRN

Yin, Lifeng; Hong, Pujiang; Zheng, Guanghai; Chen, Huayue

doi:10.3390/app12094232

Cited by 15 publications

(8 citation statements)

References 48 publications

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“…DenseNet (Densely Connected Convolutional Network) is a deep convolutional neural network structure proposed by Gao et al in 2019 [ 36 ]. The network structure of the DenseNet series is shown in Fig 3 .…”

Section: Methodsmentioning

confidence: 99%

Multimodal deep learning-based drought monitoring research for winter wheat during critical growth stages

Yao,

Wu,

Liu

et al. 2024

PLoS ONE

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Wheat is a major grain crop in China, accounting for one-fifth of the national grain production. Drought stress severely affects the normal growth and development of wheat, leading to total crop failure, reduced yields, and quality. To address the lag and limitations inherent in traditional drought monitoring methods, this paper proposes a multimodal deep learning-based drought stress monitoring S-DNet model for winter wheat during its critical growth periods. Drought stress images of winter wheat during the Rise-Jointing, Heading-Flowering and Flowering-Maturity stages were acquired to establish a dataset corresponding to soil moisture monitoring data. The DenseNet-121 model was selected as the base network to extract drought features. Combining the drought phenotypic characteristics of wheat in the field with meteorological factors and IoT technology, the study integrated the meteorological drought index SPEI, based on WSN sensors, and deep image learning data to build a multimodal deep learning-based S-DNet model for monitoring drought stress in winter wheat. The results show that, compared to the single-modal DenseNet-121 model, the multimodal S-DNet model has higher robustness and generalization capability, with an average drought recognition accuracy reaching 96.4%. This effectively achieves non-destructive, accurate, and rapid monitoring of drought stress in winter wheat.

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Section: Methodsmentioning

confidence: 99%

Multimodal deep learning-based drought monitoring research for winter wheat during critical growth stages

Yao,

Wu,

Liu

et al. 2024

PLoS ONE

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“…It is a densely connected convolutional neural network that serves different purposes in computer vision applications [73]. This dense connectivity of neurons ensures that feature maps can be re-used, thereby solving the vanishing gradient problem, improving the propagation of features and reducing the number of training parameters [74]. Figure 4 Convolutional 2D (BN-ReLU-conv2D) and Concatenation (Concat) [73].…”

Section: Densenetmentioning

confidence: 99%

“…. ) [73,74]. The bottleneck layer manages the number of channels using a compression factor to control computational efficiency while the transformation layer applies the pooling process to minimize the spatial dimension of the image size [75].…”

Section: Densenetmentioning

confidence: 99%

“…The RPN-based DenseNet was trained for roadside object detection using PASCAL VOC and MS COCO datasets. The result reported for PASCAL VOC reported 80.30% mAP and MS COCO dataset reported mAP of 55.0%, while [74] used Deep Pyramidal Residual Networks (DPRN) and DenseNet for image recognition. The DPRN was applied to facilitate the training performance of the DenseNet, through a parallel convolutional feature extraction.…”

Section: Relevant Literatures On Object Detection With Densenetmentioning

confidence: 99%

See 1 more Smart Citation

Exploring the Depths of Visual Understanding: A Comprehensive Review on Real-Time Object of Interest Detection Techniques

CHIDI,

UDANOR,

ANOLIEFO

2024

Preprint

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Object detection is complex and involved diverse requirements for different applications. In critical application such as visual impaired navigation guide and real-time surveillance for instance, identifying a particular object of instance is required and this involved a complex approach for realization. Literatures were reviewed, starting with the application of deep learning techniques for object detection; their gaps were identified and addressed using real-time object detection models literature review. From the review gaps were also detected and addressed using literature review on occlusion detection techniques. Object of instance detection in clustered scene with similar object of interest was identified as an open gap not addressed in the existing literature; even through it is vital for many applications. This research recommended an Occlusion Based Object of Instance Detection (OBOID) technique which used the spatial information to identify instant object of interest in clustered scene with many similarly object of interest. Limitation of the recommended OBOID is that it requires only system where position and distance of object is necessary to inform other decision.

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“…This method combines ACNN and FMC to achieve recognition accuracy for static images, but its recognition performance needs to be further improved for image recognition in dynamic environments. Lifeng et al (2022) proposes an image recognition method based on DenseNet and Deep Cone Residual Network (DPRN), in which the parallel feature extraction is performed using an extended convolutional module and is fused with DenseNet to construct an image recognition model. This model overcomes the problems of model complexity and large memory usage in DenseNet and can accurately recognize images, but it is difficult to identify errors in images.…”

Section: Related Researchmentioning

confidence: 99%

Image Identification and Error Correction Method for Test Report Based on Deep Reinforcement Learning and IoT Platform in Smart Laboratory

Li,

He,

Shen

et al. 2024

International Journal of Information Technologies and Systems Approach

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In order to solve the problems that most models are complex, time-consuming, and have difficulty in identifying image errors, an image identification and error correction method of test report based on deep reinforcement learning and the internet of things platform in the smart lab was proposed. Firstly, a smart lab architecture was designed based on the internet of things platform, achieving efficient operation of the laboratory through cloud edge collaboration. Then, the depth separable convolution improved convolutional neural network is used to extract image features, and the features are input into bidirectional recurrent neural networks (BiLSTM) for analysis to complete image recognition. Finally, the ICNN-BiLSTM model is used as the agent of reinforcement learning, and image error correction is completed by identifying the distance between the image and the key points of the reference image. Based on the Python platform, the proposed method was experimentally demonstrated, and the results showed that its average error correction accuracy reached 96.75%, with a processing time of 15.37s.

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A Novel Image Recognition Method Based on DenseNet and DPRN

Cited by 15 publications

References 48 publications

Multimodal deep learning-based drought monitoring research for winter wheat during critical growth stages

Multimodal deep learning-based drought monitoring research for winter wheat during critical growth stages

Exploring the Depths of Visual Understanding: A Comprehensive Review on Real-Time Object of Interest Detection Techniques

Image Identification and Error Correction Method for Test Report Based on Deep Reinforcement Learning and IoT Platform in Smart Laboratory

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