Review on deep learning techniques for marine object recognition: Architectures and algorithms

Ning, Wang; Wang, Yuanyuan; Er, Meng Joo

doi:10.1016/j.conengprac.2020.104458

Cited by 107 publications

(43 citation statements)

References 80 publications

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“…The two algorithms were used in comparative experiments, to select the best algorithm and model for target detection and recognition, as part of their underwater detection robot. In [ 27 ], a detailed review of Deep Learning-based object recognition is given, whether it is underwater or surface target recognition. Surface object recognition is mostly based on images, while underwater objects are recognized based on videos, target radiated noises [ 28 ] and acoustic noises [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

Underwater Object Recognition Using Point-Features, Bayesian Estimation and Semantic Information

Himri

Ridao

Gracias

2021

Sensors

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This paper proposes a 3D object recognition method for non-coloured point clouds using point features. The method is intended for application scenarios such as Inspection, Maintenance and Repair (IMR) of industrial sub-sea structures composed of pipes and connecting objects (such as valves, elbows and R-Tee connectors). The recognition algorithm uses a database of partial views of the objects, stored as point clouds, which is available a priori. The recognition pipeline has 5 stages: (1) Plane segmentation, (2) Pipe detection, (3) Semantic Object-segmentation and detection, (4) Feature based Object Recognition and (5) Bayesian estimation. To apply the Bayesian estimation, an object tracking method based on a new Interdistance Joint Compatibility Branch and Bound (IJCBB) algorithm is proposed. The paper studies the recognition performance depending on: (1) the point feature descriptor used, (2) the use (or not) of Bayesian estimation and (3) the inclusion of semantic information about the objects connections. The methods are tested using an experimental dataset containing laser scans and Autonomous Underwater Vehicle (AUV) navigation data. The best results are obtained using the Clustered Viewpoint Feature Histogram (CVFH) descriptor, achieving recognition rates of 51.2%, 68.6% and 90%, respectively, clearly showing the advantages of using the Bayesian estimation (18% increase) and the inclusion of semantic information (21% further increase).

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Section: Introductionmentioning

confidence: 99%

Underwater Object Recognition Using Point-Features, Bayesian Estimation and Semantic Information

Himri

Ridao

Gracias

2021

Sensors

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“…Calculate all eigenvalues and eigenvectors corresponding to formula (7) and sort the eigenvalues from small to large. K eigenvalues can be obtained:…”

Section: E Targetmentioning

confidence: 99%

“…e main research direction is to analyze the algebraic features of face images and propose a variety of new face recognition algorithms [5]. In addition to the research teams listed above, there are many other research teams that have been engaged in face recognition-related research for a long time and have made corresponding achievements and progress [6,7].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Facial Expression Recognition Using Sparse Reserved Projection Algorithm for Low Illumination Images

2021

Scientific Programming

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In this paper, a novel approach for facial expression recognition based on sparse retained projection is proposed. The locality preserving projection (LPP) algorithm is used to reduce the dimension of face image data that ensures the local near-neighbor relationship of face images. The sparse representation method is used to solve the partial occlusion of human face and the problem of light imbalance. Through sparse reconstruction, the sparse reconstruction information of expression is retained as well as the local neighborhood information of expression, which can extract more effective and judgmental internal features from the original expression data, and the obtained projection is relatively stable. The recognition results based on CK + expression database show that this method can effectively improve the facial expression recognition rate.

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“…Using object detection and recognition in scene classification have drawn much attention in the last decade with object recognition aiming to mimic the human ability to identify and distinguish between multiple objects in images or video ( Wang, Wang & Er, 2020 ). Object detection is segmented into two major subtasks; feature extraction and object classification.…”

Section: Introductionmentioning

confidence: 99%

Insights into few shot learning approaches for image scene classification

Soudy

Afify

Badr

2021

PeerJ Computer Science

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Image understanding and scene classification are keystone tasks in computer vision. The development of technologies and profusion of existing datasets open a wide room for improvement in the image classification and recognition research area. Notwithstanding the optimal performance of exiting machine learning models in image understanding and scene classification, there are still obstacles to overcome. All models are data-dependent that can only classify samples close to the training set. Moreover, these models require large data for training and learning. The first problem is solved by few-shot learning, which achieves optimal performance in object detection and classification but with a lack of eligible attention in the scene classification task. Motivated by these findings, in this paper, we introduce two models for few-shot learning in scene classification. In order to trace the behavior of those models, we also introduce two datasets (MiniSun; MiniPlaces) for image scene classification. Experimental results show that the proposed models outperform the benchmark approaches in respect of classification accuracy.

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Review on deep learning techniques for marine object recognition: Architectures and algorithms

Cited by 107 publications

References 80 publications

Underwater Object Recognition Using Point-Features, Bayesian Estimation and Semantic Information

Underwater Object Recognition Using Point-Features, Bayesian Estimation and Semantic Information

Dynamic Facial Expression Recognition Using Sparse Reserved Projection Algorithm for Low Illumination Images

Insights into few shot learning approaches for image scene classification

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