Deep learning-based visual detection of marine organisms: A survey

Ning, Wang; Chen, Tingkai; Liu, Shaoman; Wang, Rongfeng; Karimi, Hamid Reza; Lin, Yejin

doi:10.1016/j.neucom.2023.02.018

Cited by 26 publications

(11 citation statements)

References 164 publications

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“…The details of the model architecture of YOLO v5 can be found in Jocher et al [22]. In this study, YOLO v5 was chosen for two reasons: (a) it is one of the most stable versions of YOLO object detection models, and (b) it has very high accuracy reported by previous studies [23]. It is worth mentioning that other object detection models can also be used to serve the same purpose.…”

Section: Pothole Detectionmentioning

confidence: 99%

The Use of Lidar and Artificial Intelligence Algorithms for Detection and Size Estimation of Potholes

Talha,

Manasreh,

Nazzal

2024

Buildings

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Road potholes have a well-known impact on driving quality and safety. Therefore, timely mitigation of potholes is critical for the safety of road users. However, efficient and timely maintenance relies on the presence of an effective process for pothole detection. Currently, transportation agencies primarily rely on manual inspection and road user reporting. These methods are subjective, prone to inaccuracy, and some are also laborious and time-consuming. An ideal pothole detection system would be accurate, objective, automated, and relatively inexpensive. In this context, accuracy encompasses three distinct performance areas: detection, localization, and size estimation. This study explores the potential of utilizing a mobile light detection and ranging (LiDAR) for accurate detection and size estimation, along with a global navigation satellite system (GNSS) receiver for localization, to develop an effective pothole surveillance system. To achieve this objective, the study proposes a four-step framework. Firstly, the LiDAR data are processed to generate ring-wise cross-sectional images. Secondly, a deep learning object detection network is trained to predict the presence and size of potholes. Thirdly, the ring-wise inferences are aggregated to produce a final decision. Lastly, the aggregated inferences are synchronized with GNSS locations to generate inspection maps. The system’s performance was validated using multiple road strips, never seen by the model, containing potholes of different sizes and shapes. The results demonstrated the effectiveness and accuracy of the proposed system. Overall, this research contributes to the research on LiDAR-based pothole inspection by proposing a novel four-step framework and incorporating it into an end-to-end pothole detection system, which can greatly improve the efficiency of pothole maintenance and enhance the safety of road users.

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Section: Pothole Detectionmentioning

confidence: 99%

The Use of Lidar and Artificial Intelligence Algorithms for Detection and Size Estimation of Potholes

Talha,

Manasreh,

Nazzal

2024

Buildings

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“…Due to the influence of the complex imaging environment in the ocean, the underwater images appear blurred, low contrast and low resolution, therefore various image preprocessing methods (Qi et al, 2022;Zhou et al, 2022;Zhou et al, 2023a;Zhou et al, 2023b) such as image enhancement and image restoration are used first to improve classification results. Recently, significant progress has been made in underwater classification, thanks to the influence of deep learning and the creation of several methods for underwater organism detection (Chen et al, 2021;Wang et al, 2023a;Wang et al, 2023b). The research on underwater biological image classification can be mainly divided into two aspects, one is the learning of biological features, the other is the feature fusion of different levels or types.…”

Section: Underwater Image Classificationmentioning

confidence: 99%

Token-Selective Vision Transformer for fine-grained image recognition of marine organisms

Xiao

Wei

et al. 2023

Front. Mar. Sci.

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IntroductionThe objective of fine-grained image classification on marine organisms is to distinguish the subtle variations in the organisms so as to accurately classify them into subcategories. The key to accurate classification is to locate the distinguishing feature regions, such as the fish’s eye, fins, or tail, etc. Images of marine organisms are hard to work with as they are often taken from multiple angles and contain different scenes, additionally they usually have complex backgrounds and often contain human or other distractions, all of which makes it difficult to focus on the marine organism itself and identify its most distinctive features.Related workMost existing fine-grained image classification methods based on Convolutional Neural Networks (CNN) cannot accurately enough locate the distinguishing feature regions, and the identified regions also contain a large amount of background data. Vision Transformer (ViT) has strong global information capturing abilities and gives strong performances in traditional classification tasks. The core of ViT, is a Multi-Head Self-Attention mechanism (MSA) which first establishes a connection between different patch tokens in a pair of images, then combines all the information of the tokens for classification.MethodsHowever, not all tokens are conducive to fine-grained classification, many of them contain extraneous data (noise). We hope to eliminate the influence of interfering tokens such as background data on the identification of marine organisms, and then gradually narrow down the local feature area to accurately determine the distinctive features. To this end, this paper put forwards a novel Transformer-based framework, namely Token-Selective Vision Transformer (TSVT), in which the Token-Selective Self-Attention (TSSA) is proposed to select the discriminating important tokens for attention computation which helps limits the attention to more precise local regions. TSSA is applied to different layers, and the number of selected tokens in each layer decreases on the basis of the previous layer, this method gradually locates the distinguishing regions in a hierarchical manner.ResultsThe effectiveness of TSVT is verified on three marine organism datasets and it is demonstrated that TSVT can achieve the state-of-the-art performance.

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“…Underwater object detection, segmentation, and classification is a challenging research area in computer vision. Studies on the marine lives detection using deep learning techniques are summarized in [24], [25]. Also, early diagnosis of skin cancer from skin lesions using hybrid models CNN-ANN and CNN-RF is presented [14].…”

Section: Related Workmentioning

confidence: 99%

Deep Neural Networks Fused with Textures for Image Classification

Bera

Bhattacharjee

Nasipuri

2023

Lecture Notes in Networks and Systems

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Fine-grained image classification (FGIC) is a challenging task in computer vision for due to small visual differences among inter-subcategories, but, large intra-class variations. Deep learning methods have achieved remarkable success in solving FGIC. In this paper, we propose a fusion approach to address FGIC by combining global texture with local patch-based information. The first pipeline extracts deep features from various fixed-size non-overlapping patches and encodes features by sequential modeling using the long shortterm memory (LSTM). Another path computes image-level textures at multiple scales using the local binary patterns (LBP). The advantages of both streams are integrated to represent an efficient feature vector for image classification. The method is tested on eight datasets representing the human faces, skin lesions, food dishes, marine lives, etc. using four standard backbone CNNs. Our method has attained better classification accuracy over existing methods with notable margins.

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Deep learning-based visual detection of marine organisms: A survey

Cited by 26 publications

References 164 publications

The Use of Lidar and Artificial Intelligence Algorithms for Detection and Size Estimation of Potholes

The Use of Lidar and Artificial Intelligence Algorithms for Detection and Size Estimation of Potholes

Token-Selective Vision Transformer for fine-grained image recognition of marine organisms

Deep Neural Networks Fused with Textures for Image Classification

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