The complex marine environment exacerbates the challenges of object detection manifold. Marine trash endangers the aquatic ecosystem, presenting a persistent challenge. Accurate detection of marine deposits is crucial for mitigating this harm. Our work addresses underwater object detection by enhancing image quality and evaluating detection methods. We use Detectron2's backbone with various base models and configurations for this task.
We propose a novel channel stabilization technique alongside a simplified image enhancement model to reduce haze and color cast in training images, improving multi-scale object detection. Following image processing, we test different Detectron2 backbones for optimal detection accuracy. Additionally, we apply a sharpening filter with augmentation techniques to highlight object profiles for easier recognition.
Results are demonstrated on the TrashCan Dataset, both instance and material versions. The best-performing backbone method incorporates our channel stabilization and augmentation techniques. We also compare our Detectron2 detection results with the Deformable Transformer. In the instance version of TrashCan 1.0, our method achieves a 9.53% absolute increase in average precision for small objects and a 7% absolute gain in bounding box detection compared to the baseline. The code will be available on Code: https://github.com/aliman80/Underwater- Object-Detection-via-Channel-Stablization
To assist underwater object detection for better performance, image enhancement technology is often used as a pre-processing step. However, most of the existing enhancement methods tend to pursue the visual quality of an image, instead of providing effective help for detection tasks. In fact, image enhancement algorithms should be optimized with the goal of utility improvement. In this paper, to adapt to the underwater detection tasks, we proposed a lightweight dynamic enhancement algorithm using a contribution dictionary to guide low-level corrections. Dynamic solutions are designed to captur…
LUIEO: A Lightweight Model for Integrating Underwater Image Enhancement and Object Detection
School of Mathematics, North University of China（中北大学数学学院）；College of Computer and Information Technology, Shanxi University（山西大学计算机与信息技术学院）；School of Mathematics Sciences, Beijing Normal University（北京师范大学数学科学学院）
AquaFeat+: an Underwater Vision Learning-based Enhancement Method for Object Detection, Classification, and Tracking
Centro de Ciências Computacionais (C3), Universidade Federal do Rio Grande (FURG), Brazil（巴西里约格兰德联邦大学计算科学研究中心（C3））；Instituto de Ciências Exatas e Tecnologia (ICET), Universidade Federal do Amazonas (UFAM), Brazil（巴西亚马逊联邦大学精确科学与技术研究所（ICET））
Adaptive Enhancement and Dual-Pooling Sequential Attention for Lightweight Underwater Object Detection with YOLOv10
1,2Department of Computer Science；Engineering, Dhaka University of Engineering \& Technology Gazipur, 1707, Bangladesh 3Department of Computer Science；Engineering, BRAC University Dhaka, Bangladesh
FSSUWNet: Mitigating the Fragility of Pre-trained Models with Feature Enhancement for Few-Shot Semantic Segmentation in Underwater Images
School of Ocean Engineering and Technology, Sun Yat-Sen University（中山大学海洋工程学院）；Southern Marine Science and Engineering Guangdong Laboratory（广东海洋科学与工程实验室）
BG-YOLO: A Bidirectional-Guided Method for Underwater Object Detection
School of Tropical Agriculture and Forestry, Hainan University（海南大学热带农林学院）；School of Information and Communication Engineering（信息与通信工程学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Cybersecurity, Northwestern Polytechnical University（西北工业大学网络空间安全学院）
GeoCueFormer: Geometry-Guided Wavelet Representation and Prediction-Cued Dual-Stage Decoder for Underwater Semantic Segmentation
Southwest University of Science and Technology（西南科技大学）；Columbia University（哥伦比亚大学）；Carnegie Mellon University（卡内基梅隆大学）；University of California, Berkeley（加州大学伯克利分校）
Downstream Task Inspired Underwater Image Enhancement: A Perception-Aware Study from Dataset Construction to Network Design
State Key Laboratory of Physical Oceanography, Ocean University of China（中国海洋大学物理海洋国家重点实验室）；Department of Electrical and Computer Engineering, Mississippi State University（密苏里州立大学电子与计算机工程系）
MARIS: Marine Open-Vocabulary Instance Segmentation with Geometric Enhancement and Semantic Alignment
University of Science and Technology of China（中国科学技术大学）；Institute of Artificial Intelligence (TeleAI)（人工智能研究院（TeleAI））；China Telecom（中国电信）；Fudan University（复旦大学）；Northwestern Polytechnical University（西北工业大学）
Underwater image enhancement is such an important vision task due to its significance in marine engineering and aquatic robot. It is usually work as a pre-processing step to improve the performance of high level vision tasks such as underwater object detection. Even though many previous works show the underwater image enhancement algorithms can boost the detection accuracy of the detectors, no work specially focus on investigating the relationship between these two tasks. This is mainly because existing underwater datasets lack either bounding box annotations or high quality reference images, …
University of Leicester（莱斯特大学）；Ocean University of China（中国海洋大学）；Queen Mary University of London（伦敦玛丽女王大学）
Underwater image enhancement, as a pre-processing step to improve the accuracy of the following object detection task, has drawn considerable attention in the field of underwater navigation and ocean exploration. However, most of the existing underwater image enhancement strategies tend to consider enhancement and detection as two independent modules with no interaction, and the practice of separate optimization does not always help the underwater object detection task. In this paper, we propose two perceptual enhancement models, each of which uses a deep enhancement model with a detection per…
Underwater image suffer from color cast, low contrast and hazy effect due to light absorption, refraction and scattering, which degraded the high-level application, e.g, object detection and object tracking. Recent learning-based methods demonstrate astonishing performance on underwater image enhancement, however, most of these works use synthetic pair data for supervised learning and ignore the domain gap to real-world data. To solve this problem, we propose a domain adaptation framework for underwater image enhancement via content and style separation, different from prior works of domain ad…
City University of Hong Kong（香港城市大学）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；Tianjin University（天津大学）；Beijing Jiaotong University（北京交通大学）
Underwater image enhancement has been attracting much attention due to its significance in marine engineering and aquatic robotics. Numerous underwater image enhancement algorithms have been proposed in the last few years. However, these algorithms are mainly evaluated using either synthetic datasets or few selected real-world images. It is thus unclear how these algorithms would perform on images acquired in the wild and how we could gauge the progress in the field. To bridge this gap, we present the first comprehensive perceptual study and analysis of underwater image enhancement using large…
The experimental results of improved underwater image enhancement algorithms based on partial differential equations (PDEs) are presented in this report. This second work extends the study of previous work and incorporating several improvements into the revised algorithm. Experiments show the evidence of the improvements when compared to previously proposed approaches and other conventional algorithms found in the literature.
Due to the absorption and scattering effects of the water, underwater images tend to suffer from many severe problems, such as low contrast, grayed out colors and blurring content. To improve the visual quality of underwater images, we proposed a novel enhancement model, which is a trainable end-to-end neural model. Two parts constitute the overall model. The first one is a non-parameter layer for the preliminary color correction, then the second part is consisted of parametric layers for a self-adaptive refinement, namely the channel-wise linear shift. For better details, contrast and colorfu…
Australian National University（澳大利亚国立大学）；Civil Aviation University of China（中国民航大学）
In an underwater scene, wavelength-dependent light absorption and scattering degrade the visibility of images, causing low contrast and distorted color casts. To address this problem, we propose a convolutional neural network based image enhancement model, i.e., UWCNN, which is trained efficiently using a synthetic underwater image database. Unlike the existing works that require the parameters of underwater imaging model estimation or impose inflexible frameworks applicable only for specific scenes, our model directly reconstructs the clear latent underwater image by leveraging on an automati…
This report presents the results of a multi-scale wavelet based scheme for single image de-hazing and underwater image enhancement. The scheme is fast and highly localized in addition to global enhancement of hazy images. A PDE-based formulation enables additional versatility as the iterative nature allows more flexibility for various types of images. Visual and objective results from experiments indicate that the proposed approach competes favourably or surpasses most of the state-of-the-art approaches.
ASUS Intelligent Cloud Services, Asustek Computer Inc.（华硕电脑股份有限公司智能云服务）；National Taiwan University（国立台湾大学）
Underwater image enhancement is an important low-level computer vision task for autonomous underwater vehicles and remotely operated vehicles to explore and understand the underwater environments. Recently, deep convolutional neural networks (CNNs) have been successfully used in many computer vision problems, and so does underwater image enhancement. There are many deep-learning-based methods with impressive performance for underwater image enhancement, but their memory and model parameter costs are hindrances in practical application. To address this issue, we propose a lightweight adaptive f…
Visual enhancement and 3D representation for underwater scenes: a review
Visual Information Laboratory, University of Bristol（视觉信息实验室，布里斯托尔大学）；Submerged Resources Center, National Park Service（水下资源中心，国家公园服务）；Marine Imaging Technologies, LLC（海洋成像技术有限公司）；Gates Underwater Products, Inc（盖茨水下产品公司）；Esprit film and television Ltd（Esprit电影和电视有限公司）
Underwater image enhancement is such an important low-level vision task with many applications that numerous algorithms have been proposed in recent years. These algorithms developed upon various assumptions demonstrate successes from various aspects using different data sets and different metrics. In this work, we setup an undersea image capturing system, and construct a large-scale Real-world Underwater Image Enhancement (RUIE) data set divided into three subsets. The three subsets target at three challenging aspects for enhancement, i.e., image visibility quality, color casts, and higher-le…
Owing to refraction, absorption, and scattering of light by suspended particles in water, raw underwater images suffer from low contrast, blurred details, and color distortion. These characteristics can significantly interfere with the visibility of underwater images and the result of visual tasks, such as segmentation and tracking. To address this problem, we propose a new robust adversarial learning framework via physics model based feedback control and domain adaptation mechanism for enhancing underwater images to get realistic results. A new method for simulating underwater-like training d…
A main challenge faced in the deep learning-based Underwater Image Enhancement (UIE) is that the ground truth high-quality image is unavailable. Most of the existing methods first generate approximate reference maps and then train an enhancement network with certainty. This kind of method fails to handle the ambiguity of the reference map. In this paper, we resolve UIE into distribution estimation and consensus process. We present a novel probabilistic network to learn the enhancement distribution of degraded underwater images. Specifically, we combine conditional variational autoencoder with …
Underwater image enhancement has attracted much attention due to the rise of marine resource development in recent years. Benefit from the powerful representation capabilities of Convolution Neural Networks(CNNs), multiple underwater image enhancement algorithms based on CNNs have been proposed in the last few years. However, almost all of these algorithms employ RGB color space setting, which is insensitive to image properties such as luminance and saturation. To address this problem, we proposed Underwater Image Enhancement Convolution Neural Network using 2 Color Space (UICE^2-Net) that eff…
In this paper, we present a conditional generative adversarial network-based model for real-time underwater image enhancement. To supervise the adversarial training, we formulate an objective function that evaluates the perceptual image quality based on its global content, color, local texture, and style information. We also present EUVP, a large-scale dataset of a paired and unpaired collection of underwater images (of `poor' and `good' quality) that are captured using seven different cameras over various visibility conditions during oceanic explorations and human-robot collaborative experime…
HUPE: Heuristic Underwater Perceptual Enhancement with Semantic Collaborative Learning
School of Software Engineering, Dalian University of Technology（大连理工大学软件学院）；College of Information Science and Technology, Dalian Maritime University（大连海事大学信息科学技术学院）；Pazhou Laboratory (Huangpu)（琶洲实验室（黄埔））
A Physical Model-Guided Framework for Underwater Image Enhancement and Depth Estimation
Hangzhou Innovation Institute, Beihang University（北京航空航天大学杭州创新研究院）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）
3D-USE: From Image-Level to Scene-Level Underwater Enhancement
College of Computer Science, Nankai University（南开大学计算机学院）；NKIARI（南开大学人工智能研究院（深圳））；School of Automation, Southeast University（东南大学自动化学院）；Advanced Ocean Institute of Southeast University, Nantong（东南大学南通先进海洋研究院）
Five A$^{+}$ Network: You Only Need 9K Parameters for Underwater Image Enhancement
National University of Singapore（新加坡国立大学）；ZJU-UIUC Institute, Zhejiang University（浙江大学ZJU-UIUC研究院）；Fujian Provincial Key Laboratory of Oceanic Information Perception and Intelligent Processing（福建省海洋信息感知与智能处理重点实验室）
Institute of Artificial Intelligence (TeleAI), China Telecom（人工智能研究院（TeleAI），中国电信）；College of Computer and Control Engineering, Northeast Forestry University（东北林业大学计算机与控制工程学院）；College of Future Information Technology, Fudan University（复旦大学未来信息技术学院）；School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University（西北工业大学人工智能、光学与电子学院（iOPEN））；Fujian Ocean Innovation Center, Xiamen（福建海洋创新中心，厦门）
Underwater image enhancement has become an attractive topic as a significant technology in marine engineering and aquatic robotics. However, the limited number of datasets and imperfect hand-crafted ground truth weaken its robustness to unseen scenarios, and hamper the application to high-level vision tasks. To address the above limitations, we develop an efficient and compact enhancement network in collaboration with a high-level semantic-aware pretrained model, aiming to exploit its hierarchical feature representation as an auxiliary for the low-level underwater image enhancement. Specifical…
CARNet: Collaborative Adversarial Resilience for Robust Underwater Image Enhancement and Perception
School of Software Technology, Dalian University of Technology（大连理工大学软件学院）；College of Information Science and Technology, Dalian Maritime University（大连海事大学信息科学技术学院）；School of Mechanical Engineering, Dalian University of Technology（大连理工大学机械工程学院）
Learning A Physical-aware Diffusion Model Based on Transformer for Underwater Image Enhancement
School of Intelligence Science and Technology, Nanjing University（智能科学与技术学院，南京大学）；School of Artificial Intelligence, Nanjing Normal University（人工智能学院，南京师范大学）
Raw underwater images are degraded due to wavelength dependent light attenuation and scattering, limiting their applicability in vision systems. Another factor that makes enhancing underwater images particularly challenging is the diversity of the water types in which they are captured. For example, images captured in deep oceanic waters have a different distribution from those captured in shallow coastal waters. Such diversity makes it hard to train a single model to enhance underwater images. In this work, we propose a novel model which nicely handles the diversity of water during the enhanc…
MAC-Lookup: Multi-Axis Conditional Lookup Model for Underwater Image Enhancement
School of Computer Science and Engineering, Huizhou University（惠州大学计算机科学与工程学院）；University of Chinese Academy of Sciences（中国科学院大学）；Xi’an Jiaotong-Liverpool University（西安交通大学利物浦大学）
Underwater Variable Zoom: Depth-Guided Perception Network for Underwater Image Enhancement
School of Computer Science and Technology, Dalian University of Technology（大连理工大学计算机科学与技术学院）；University of New South Wales（新南威尔士大学）；School of Computer Science and Technology, Shandong Technology and Business University（山东工商学院计算机科学与技术学院）；School of Information and Communication Engineering, Dalian Minzu University（大连民族大学信息与通信工程学院）；School of Innovation Experiment, Dalian University of Technology（大连理工大学创新实验学院）
A Generative Data Framework with Authentic Supervision for Underwater Image Restoration and Enhancement
Institute of Artificial Intelligence (TeleAI), China Telecom（人工智能研究院（TeleAI），中国电信）；Department of Automation, Shanghai Jiao Tong University（自动化系，上海交通大学）；School of Artificial Intelligence, Optics and ElectroNics (iOPEN), Northwestern Polytechnical University（人工智能、光学与电子学学院（iOPEN），西北工业大学）；Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences（西安光学精密机械研究所，中国科学院）
