An Improved Deep Residual Network-Based Semantic Simultaneous Localization and Mapping Method for Monocular Vision Robot

Ni, Jianjun; Gong, Tao; Gu, Ya-fei; Zhu, Jiasong; Fan, Xiaopeng

doi:10.1155/2020/7490840

Cited by 20 publications

(14 citation statements)

References 39 publications

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“…The authors used Caltech101 and Caltech256 datasets testing the model and have demonstrated superior results by Tree-CNN. Some other related studies can be found in [20][21][22][23].…”

Section: Literature Studymentioning

confidence: 99%

Lifelong Machine Learning for Regional-Based Image Classification in Open Datasets

2020

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Deep Learning algorithms are becoming common in solving different supervised and unsupervised learning problems. Different deep learning algorithms were developed in last decade to solve different learning problems in different domains such as computer vision, speech recognition, machine translation, etc. In the research field of computer vision, it is observed that deep learning has become overwhelmingly popular. In solving computer vision related problems, we first take a CNN (Convolutional Neural Network) which is trained from scratch or some times a pre-trained model is taken and further fine-tuned based on the dataset that is available. The problem of training the model from scratch on new datasets suffers from catastrophic forgetting. Which means that when a new dataset is used to train the model, it forgets the knowledge it has obtained from an existing dataset. In other words different datasets does not help the model to increase its knowledge. The problem with the pre-trained models is that mostly CNN models are trained on open datasets, where the data set contains instances from specific regions. This results into predicting disturbing labels when the same model is used for instances of datasets collected in a different region. Therefore, there is a need to find a solution on how to reduce the gap of Geo-diversity in different computer vision problems in developing world. In this paper, we explore the problems of models that were trained from scratch along with models which are pre-trained on a large dataset, using a dataset specifically developed to understand the geo-diversity issues in open datasets. The dataset contains images of different wedding scenarios in South Asian countries. We developed a Lifelong CNN that can incrementally increase knowledge i.e., the CNN learns labels from the new dataset but includes the existing knowledge of open data sets. The proposed model demonstrates highest accuracy compared to models trained from scratch or pre-trained model.

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“…The authors used Caltech101 and Caltech256 datasets testing the model and have demonstrated superior results by Tree-CNN. Some other related studies can be found in [20][21][22][23].…”

Section: Literature Studymentioning

confidence: 99%

Lifelong Machine Learning for Regional-Based Image Classification in Open Datasets

2020

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“…en, according to the dynamic probability of the given category label, the dynamic probability of the object in the image is presented. After obtaining accurate semantic labels and dynamic probabilities [18][19][20], combining with the position of objects output by YOLO, feature filtering is carried out for key frames and landmark points, so as to minimize the impact of dynamic objects on map construction and location.…”

Section: Local Mapping Based On Object Level Informationmentioning

confidence: 99%

Semantic Optimization of Feature-Based SLAM

Yin

Gao

Sun

2021

Mathematical Problems in Engineering

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The purpose of this paper is to provide reasonable recommendation and removal of inappropriate information for SLAM (Simultaneous Localization and Mapping) technology based on feature method. The methodology is to propose a semantic recognition of environment objects in the natural scene through object detection, which is a kind of bag of word method in SLAM problem between the key frames and object level, the method of establishing key frames, and the relationship between the target object levels, through the practical signiﬁcance of the target object level to judge the merits of the target object level information, and then combined with key frames in the visual SLAM relations with relevant information, so as to get object level targets in each key frame and the relationship between the relevant information, so as to achieve through the object level semantic information to judge the merits of the key frames and screening, as well as to the key frames to judge the merits of the relevant information and screening purpose. The finding of the study is the above method can retain the information of high reliability and good stability for visual SLAM and process the key frames with poor reliability or low stability and the information related to key frames.

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Section: Introductionmentioning

confidence: 99%

“…The target detection algorithm based on deep learning has the advantages of high detection accuracy and strong robustness. It is widely used in environmental monitoring [7], autonomous driving [8], UAV scene analysis [9] and other scenarios. However, due to the low quality of underwater imaging, complex underwater environment, the different sizes or shapes and overlapping or occlusion of underwater organisms, the general target detection algorithm based on deep learning does not have a good detection effect on underwater organisms.…”

Section: Introductionmentioning

confidence: 99%

Underwater Biological Detection Algorithm Based on Improved Faster-RCNN

Shi

et al. 2021

Water

Self Cite

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Underwater organisms are an important part of the underwater ecological environment. More and more attention has been paid to the perception of underwater ecological environment by intelligent means, such as machine vision. However, many objective reasons affect the accuracy of underwater biological detection, such as the low-quality image, different sizes or shapes, and overlapping or occlusion of underwater organisms. Therefore, this paper proposes an underwater biological detection algorithm based on improved Faster-RCNN. Firstly, the ResNet is used as the backbone feature extraction network of Faster-RCNN. Then, BiFPN (Bidirectional Feature Pyramid Network) is used to build a ResNet–BiFPN structure which can improve the capability of feature extraction and multi-scale feature fusion. Additionally, EIoU (Effective IoU) is used to replace IoU to reduce the proportion of redundant bounding boxes in the training data. Moreover, K-means++ clustering is used to generate more suitable anchor boxes to improve detection accuracy. Finally, the experimental results show that the detection accuracy of underwater biological detection algorithm based on improved Faster-RCNN on URPC2018 dataset is improved to 88.94%, which is 8.26% higher than Faster-RCNN. The results fully prove the effectiveness of the proposed algorithm.

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An Improved Deep Residual Network-Based Semantic Simultaneous Localization and Mapping Method for Monocular Vision Robot

Cited by 20 publications

References 39 publications

Lifelong Machine Learning for Regional-Based Image Classification in Open Datasets

Lifelong Machine Learning for Regional-Based Image Classification in Open Datasets

Semantic Optimization of Feature-Based SLAM

Underwater Biological Detection Algorithm Based on Improved Faster-RCNN

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