Research on Image Target Detection and Recognition Based on Deep Learning

Yuan, Nanqi; Kang, Byeong Ho; Xu, Shuxiang; Yang, Wenli; Ji, Ruixuan

doi:10.1109/iciscae.2018.8666851

Cited by 5 publications

(2 citation statements)

References 4 publications

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“…However, as deep learning continues to progress, we are now witnessing a shift in the landscape. It has not only demonstrated significant performance in the field of computer vision ( Yuan et al, 2018 ; Zhang and Zheng, 2022 ) and natural language processing ( Lauriola and Aiolli, 2022 ), but has also gained widespread popularity in biomedical signal processing ( Rahman et al, 2021 ). Initially, Wang et al (2022) utilized convolutional neural network (CNN) to classify positive, neutral, and negative emotions.…”

Section: Introductionmentioning

confidence: 99%

EEG-based emotion recognition using graph convolutional neural network with dual attention mechanism

Chen,

Liao,

Dai

et al. 2024

Front. Comput. Neurosci.

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EEG-based emotion recognition is becoming crucial in brain-computer interfaces (BCI). Currently, most researches focus on improving accuracy, while neglecting further research on the interpretability of models, we are committed to analyzing the impact of different brain regions and signal frequency bands on emotion generation based on graph structure. Therefore, this paper proposes a method named Dual Attention Mechanism Graph Convolutional Neural Network (DAMGCN). Specifically, we utilize graph convolutional neural networks to model the brain network as a graph to extract representative spatial features. Furthermore, we employ the self-attention mechanism of the Transformer model which allocates more electrode channel weights and signal frequency band weights to important brain regions and frequency bands. The visualization of attention mechanism clearly demonstrates the weight allocation learned by DAMGCN. During the performance evaluation of our model on the DEAP, SEED, and SEED-IV datasets, we achieved the best results on the SEED dataset, showing subject-dependent experiments’ accuracy of 99.42% and subject-independent experiments’ accuracy of 73.21%. The results are demonstrably superior to the accuracies of most existing models in the realm of EEG-based emotion recognition.

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Section: Introductionmentioning

confidence: 99%

EEG-based emotion recognition using graph convolutional neural network with dual attention mechanism

Chen,

Liao,

Dai

et al. 2024

Front. Comput. Neurosci.

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“…One is the background segmentation of the image, and then the theoretical research algorithm is designed; the features are extracted manually and sent to the classifier to recognize the feature image [14]. The second is image recognition based on deep learning, which sends the image into a convolutional neural network and automatically extracts features to recognize the image [15][16][17][18][19]. In recent years, many researchers have applied deep learning to forest fire detection.…”

Section: Introductionmentioning

confidence: 99%

A High-Precision Ensemble Model for Forest Fire Detection in Large and Small Targets

Qian,

Bai,

Jiao

et al. 2023

Forests

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Forest fires are major forestry disasters that cause loss of forest resources, forest ecosystem safety, and personal injury. It is often difficult for current forest fire detection models to achieve high detection accuracy on both large and small targets at the same time. In addition, most of the existing forest fire detection models are single detection models, and using only a single model for fire detection in a complex forest environment has a high misclassification rate, and the accuracy rate needs to be improved. Aiming at the above problems, this paper designs two forest fire detection models (named WSB and WSS) and proposes an integrated learning-based forest fire detection model (named WSB_WSS), which also obtains high accuracy in the detection of forest fires with large and small targets. In order to help the model predict the location and size of forest fire targets more accurately, a new edge loss function, Wise-Faster Intersection over Union (WFIoU), is designed in this paper, which effectively improves the performance of the forest fire detection algorithm. The WSB model introduces the Simple-Attention-Module (SimAM) attention mechanism to make the image feature extraction more accurate and introduces the bi-directional connectivity and cross-layer feature fusion to enhance the information mobility and feature expression ability of the feature pyramid network. The WSS model introduces the Squeeze-and-Excitation Networks (SE) attention mechanism so that the model can pay more attention to the most informative forest fire features and suppress unimportant features, and proposes Spatial Pyramid Pooling-Fast Cross Stage Partial Networks (SPPFCSPC) to enable the network to extract features better and speed up the operation of the model. The experimental findings demonstrate that the WSB model outperforms other approaches in the context of identifying forest fires characterized by small-scale targets, achieving a commendable accuracy rate of 82.4%, while the WSS model obtains a higher accuracy of 92.8% in the identification of large target forest fires. Therefore, in this paper, a more efficient forest fire detection model, WSB_WSS, is proposed by integrating the two models through the method of Weighted Boxes Fusion (WBF), and the accuracy of detecting forest fires characterized by small-scale targets attains 83.3%, while for forest fires with larger dimensions, the accuracy reaches an impressive 93.5%. This outcome effectively leverages the strengths inherent in both models, consequently achieving the dual objective of high-precision detection for both small and large target forest fires concurrently.

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