A Reinforcement Learning Paradigm of Configuring Visual Enhancement for Object Detection in Underwater Scenes

Wang, Hao; Sun, Shixin; Wang, Jian; Ren, Peng

doi:10.1109/joe.2022.3226202

Cited by 55 publications

(10 citation statements)

References 66 publications

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“…Zhang et al (2023) proposed a generative adversarial-driven cross-perception network (GACNet) for wheat variety identification and authentication. Wang et al (2023b) proposed reinforcement learning with visual enhancement for object detection in underwater scenes to gradually enhance visual images to improve detection results. Guo et al (2022) proposed a fully automated model compression framework called 3D-Pruning (3DP), which aims to achieve efficient 3D action recognition.…”

Section: Image Object Recognitionmentioning

confidence: 99%

Multi-scale aware turbulence network for underwater object recognition

Zhou,

Cai,

Jia

et al. 2024

Front. Mar. Sci.

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Underwater imagery is subject to distortion, and the presence of turbulence in the fluid medium poses difficulties in accurately discerning objects. To tackle these challenges pertaining to feature extraction, this research paper presents a novel approach called the multi-scale aware turbulence network (MATNet) method for underwater object identification. More specifically, the paper introduces a module known as the multi-scale feature extraction pyramid network module, which incorporates dense linking strategies and position learning strategies to preprocess object contour features and texture features. This module facilitates the efficient extraction of multi-scale features, thereby enhancing the effectiveness of the identification process. Following that, the extracted features undergo refinement through comparison with positive and negative samples. Ultimately, the study introduces multi-scale object recognition techniques and establishes a multi-scale object recognition network for the precise identification of underwater objects, utilizing the enhanced multi-scale features. This process entails rectifying the distorted image and subsequently recognizing the rectified object. Extensive experiments conducted on an underwater distorted image enhancement dataset demonstrate that the proposed method surpasses state-of-the-art approaches in both qualitative and quantitative evaluations.

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Section: Image Object Recognitionmentioning

confidence: 99%

Multi-scale aware turbulence network for underwater object recognition

Zhou,

Cai,

Jia

et al. 2024

Front. Mar. Sci.

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“…Chen et al [26] proposed the sample-weighted hyper network (SWIPENET) and curriculum multi-class Adaboost to overcome ambiguity in underwater object detection in the presence of a large number of small objects by generating multiple high-resolution and semantically rich feature maps from the backbone network of SWIPENET. Wang et al [27] proposed a paradigm for reinforcement learning to configure visual enhancement for object detection in underwater scenes, and experimentally verified its effectiveness. Zhang et al [28] defined a new intersection over union loss based on YOLOv4 and achieved an accuracy improvement on the URPC dataset, but the GigaFLOPs (GFLOPs) were quite large and speed was not satisfactory.…”

Section: Related Work 21 One-stage Underwater Object Detection Algori...mentioning

confidence: 99%

EF-UODA: Underwater Object Detection Based on Enhanced Feature

Zu,

Zhang,

et al. 2024

JMSE

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The ability to detect underwater objects accurately is important in marine environmental engineering. Although many kinds of underwater object detection algorithms with relatively high accuracy have been proposed, they involve a large number of parameters and floating point operations (FLOPs), and often fail to yield satisfactory results in complex underwater environments. In light of the demand for an algorithm with the capability to extract high-quality features in complex underwater environments, we proposed a one-stage object detection algorithm called the enhanced feature-based underwater object detection algorithm (EF-UODA), which was based on the architecture of Next-ViT, the loss function of YOLOv8, and Ultralytics. First, we developed a highly efficient module for convolutions, called efficient multi-scale pointwise convolution (EMPC). Second, we proposed a feature pyramid architecture called the multipath fast fusion-feature pyramid network (M2F-FPN) based on different modes of feature fusion. Finally, we integrated the Next-ViT and the minimum point distance intersection over union loss functions in our proposed algorithm. Specifically, on the URPC2020 dataset, EF-UODA surpasses the state-of-the-art (SOTA) convolution-based object detection algorithm YOLOv8X by 2.9% mean average precision (mAP), and surpasses the SOTA ViT-based object detection algorithm real-time detection transformer (RT-DETR) by 2.1%. Meanwhile, it achieves the lowest FLOPs and parameters. The results of extensive experiments showed that EF-UODA had excellent feature extraction capability, and was adequately balanced in terms of the number of FLOPs and parameters.

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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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A Reinforcement Learning Paradigm of Configuring Visual Enhancement for Object Detection in Underwater Scenes

Cited by 55 publications

References 66 publications

Multi-scale aware turbulence network for underwater object recognition

Multi-scale aware turbulence network for underwater object recognition

EF-UODA: Underwater Object Detection Based on Enhanced Feature

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

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