Research on Object Detection Algorithm Based on Multilayer Information Fusion

Baoyuan, Chen; Shen, Yukun; Sun, Kun

doi:10.1155/2020/9076857

Cited by 7 publications

(4 citation statements)

References 25 publications

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“…e system has begun to explore the network fault diagnosis topics of major scientific research institutes and has begun to develop from expert systems to diversified, cross-integrated, and achieved certain results [6]. However, according to the report of Samarthrao et al on the whole, the research and exploration of network fault diagnosis system, from 0 to 1, will gradually develop and will continue to move towards the goal of further improvement [7].…”

Section: Literature Reviewmentioning

confidence: 99%

Computer Communication Network Fault Detection Based on Improved Neural Network Algorithm

2022

Mobile Information Systems

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In order to meet the new requirements of fault diagnosis response and intelligent degree in the current computer network, a fault detection of computer communication network based on an improved neural network algorithm is proposed. First, from the perspective of deep learning, based on the KDD99 data set, the network fault diagnosis method based on the convolutional neural network model is studied, and the data conversion operation of grayscale matrixed raw data is proposed. And experiments are carried out, the convolutional neural network structure is designed according to the scale of data features, a series of optimization studies including discarding learning, gradient optimization algorithm, and data enhancement based on this is carried out, and the establishment of the entire fault diagnosis model is completed. The experimental results show that, in the diagnostic model designed in this paper, the Tanh activation function is used in the first fully connected layer to achieve the best convergence speed. During the training process, it can start to converge after about 24 iterations, and the accuracy rate of the model training process can reach 98.1%, verifying the correctness and superiority of the algorithm and model.

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Section: Literature Reviewmentioning

confidence: 99%

Computer Communication Network Fault Detection Based on Improved Neural Network Algorithm

2022

Mobile Information Systems

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“…Applied two DenseNet models for object detection using Pascal VOC2007 dataset [85]. First is the F-RCNN [22,23] based DenseNet where the dense network extracts multiscale features from the images, and then the F-RCNN acts as the predictor using object regions using a bounding box.…”

Section: Relevant Literatures On Object Detection With Densenetmentioning

confidence: 99%

“…While the reviewed literature discussed how the various deep learning techniques were applied in many cases for object detection [77,78,85,89], through image classification, gaps such as lack of instructiveness, delay, occlusion, need or improved overall performance, are some of the main gaps identified. To bridge this gap, one-stage object detection models are specially tailored for real-time classification problems.…”

Section: Bridging the Gaps With Real-time Object Detection Modelsmentioning

confidence: 99%

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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“…e research of Ren. et al [33] and Chen et al [34] proved that expanding the receptive field of the model could improve accuracy. He et al [35]…”

Section: Strength Receptive Field Blockmentioning

confidence: 99%

Earf-YOLO: An Efficient Attention Receptive Field Model for Recognizing Symbols of Zhuang Minority Patterns

Wang

Yan

Qin

et al. 2022

Mathematical Problems in Engineering

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As for recognizing Zhuang minority pattern symbols, current recognition models often cause high computational overhead and low accuracy since Zhuang minority pattern symbols have large feature vectors and some complex features. In this paper, we present the efficient attention receptive field you only look once (Earf-YOLO), a new scheme to address those problems. Firstly, a global-local-transformer (GLocalT) structure is proposed, through which other control systems are introduced into the axial self-attention module, and global-local training strategies are also designed. The structure can use other control systems to compensate for the lost feature information along the height, width, and channel axes. The global-local training strategy can encode long-term dependencies between features and reduce local information loss, fully illustrating that the structure has high feature expression ability. Besides, strength receptive field block (SRFB) is suggested to use the dilated convolution to control the receptive field’s eccentricity and enrich the feature information of the receptive field during its training. With more branches, it can better extract multiscale features, enrich the feature space of the convolution block, and reparametrize multibranch during prediction to fuse them into the main branch, all of which contribute to the improvement of the model performance. Finally, some advanced training techniques are adopted to enhance the detection effect further. In the end, comparative experiments are conducted on the datasets of Zhuang pattern symbols and PASCAL VOC, whose results indicate that the AP and FPS of the suggested model reach their highest values, manifesting its high efficiency.

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Research on Object Detection Algorithm Based on Multilayer Information Fusion

Cited by 7 publications

References 25 publications

Computer Communication Network Fault Detection Based on Improved Neural Network Algorithm

Computer Communication Network Fault Detection Based on Improved Neural Network Algorithm

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

Earf-YOLO: An Efficient Attention Receptive Field Model for Recognizing Symbols of Zhuang Minority Patterns

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