Automatic underwater moving object detection using multi‐feature integration framework in complex backgrounds

Vasamsetti, Srikanth; Setia, Supriya; Mittal, Neerja; Sardana, Harish Kumar; Babbar, Geetanjali

doi:10.1049/iet-cvi.2017.0013

Cited by 17 publications

(4 citation statements)

References 42 publications

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“…The depth and color-based features are combined by Chen and Li (2018) for salient object detection. Similarly, Vasamsetti et al (2018) proposed an underwater moving object detection approach by integrating local features with color and motion In many instances, it is observed that the ROVs and AUVs are working with limited power supply and computing capability. Hence the lightweight embedded CNN models are suitable for such underwater applications.…”

Section: Deep Learning Based Object Detection Techniquesmentioning

confidence: 99%

Passive Visual Underwater Surveillance: A Survey

Subudhi

Kapoor

Rout

et al. 2023

Preprint

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The objective of this article is to provide a detailed review of the state-of-the-art underwater surveillance process and the new trends of the same. Underwater surveillance has recently getting lots of attention because of its potential applications including the security of the coastal border, effective fish farming, deep-sea exploration, preservation of rare aquatic animals, etc. In the case of underwater, the light that is sensed by the camera got degraded due to many underwater phenomena such as haze, low illumination, scattering, absorption, diffraction, and refraction. The actual color of the object, as well as the scene, gets degraded as the light can not travel deep underwater. In a situation, where the scene of view is not clear, it is very difficult to detect the moving objects present in the scene. Further, the identification of the object of concern and tracking of that becomes more challenging. In this survey, we categorize underwater surveillance as a combination of the three blocks: enhancement, object detection, and object tracking. We categorize all these three blocks based on the underwater complexity and motion models considered. In this article, we tried to enumerate the most detailed descriptions of each category. We also discuss the future directions of research in the area of underwater surveillance.

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Section: Deep Learning Based Object Detection Techniquesmentioning

confidence: 99%

Passive Visual Underwater Surveillance: A Survey

Subudhi

Kapoor

Rout

et al. 2023

Preprint

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show abstract

“…The initial background segmentation utilised data from consecutive video frames to train the Gaussian mixture models and Kalman filters, so this approach required significant parallelisable computing resources. (Vasamsetti et al, 2018) used a combination of colour and texture information across three frames of a video to detect moving objects in underwater scenes. Texture information is extracted for objects using a novel multi-frame triplet feature that compares neighbouring pixel intensity values across consecutive frames to segment moving objects.…”

Section: Related Workmentioning

confidence: 99%

Image Feature Extraction Methods for Structure Detection From Underwater Imagery

Roberts,

Helmholz,

Parnum

et al. 2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The use of autonomous underwater vehicles (AUVs) for surveying underwater infrastructure presents a potential cost saving in comparison to remotely operated vehicles (ROVs). One of the challenges when processing images of underwater structures captured by an AUV, is that vast number of images captured during the mission usually do not show the structure. For instance, images captured during the dive to the structure or of the sea floor, or of the deep sea facing away from the structure. Too many images captured, without relevant information for a 3D reconstruction of the structure, leads to increased processing time and issues during the reconstruction process. There are two solutions to reduce the images to only images showing the structure. Firstly, only images of the structure are captured in the first place or remove images that are not useful after the capture and before further processing. This study developed and evaluated techniques that would enable the first strategy to be applied in an AUV. To apply this strategy in an AUV, would require an on-board structure detection system to ensure that they are correctly orientated for capturing useful footage during a survey mission. However, the marine environment poses several challenges to image-based object detection. Furthermore, small AUVs have limited power and computational resources available while deployed on a mission. To investigate the suitability of creating a lightweight structure detection model for the purpose of image evaluation, three computationally efficient image feature extraction methods (colour moments, local binary patterns (LBP), and Haar wavelet decomposition) were evaluated for their ability to distinguish underwater structures from background areas using unsupervised k-means models. LBP was found to be an effective method for identifying underwater structures in open water conditions. For identifying a structure against the seabed, colour moments were identified as the most effective method.

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“…Yan et al [6] introduced the concept of underwater object detection from the image sequence extracted from underwater videos based on statistical gradient coordinate model and Newton Raphson method to estimate the object position from the input underwater scenes. Vasamsetti et al [7] developed an ADA-boost based optimization approach to detect underwater objects. The Ada-boost method is tested with grayscale images and detection is achieved based on edge information.…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Network Driven Automated Underwater Object Detection

Mathias¹,

Dhanalakshmi²,

Kumar³

et al. 2022

Computers, Materials &Amp; Continua

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Object recognition and computer vision techniques for automated object identification are attracting marine biologist's interest as a quicker and easier tool for estimating the fish abundance in marine environments. However, the biggest problem posed by unrestricted aquatic imaging is low luminance, turbidity, background ambiguity, and context camouflage, which make traditional approaches rely on their efficiency due to inaccurate detection or elevated false-positive rates. To address these challenges, we suggest a systemic approach to merge visual features and Gaussian mixture models with You Only Look Once (YOLOv3) deep network, a coherent strategy for recognizing fish in challenging underwater images. As an image restoration phase, pre-processing based on diffraction correction is primarily applied to frames. The YOLOv3 based object recognition system is used to identify fish occurrences. The objects in the background that are camouflaged are often overlooked by the YOLOv3 model. A proposed Bi-dimensional Empirical Mode Decomposition (BEMD) algorithm, adapted by Gaussian mixture models, and integrating the results of YOLOv3 improves detection efficiency of the proposed automated underwater object detection method. The proposed approach was tested on four challenging video datasets, the Life Cross Language Evaluation Forum (CLEF) benchmark from the F4K data repository, the University of Western Australia (UWA) dataset, the bubble vision dataset and the DeepFish dataset. The accuracy for fish identification is 98.5 percent, 96.77 percent, 97.99 percent and 95.3 percent respectively for the various datasets which demonstrate the feasibility of our proposed automated underwater object detection method.

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Automatic underwater moving object detection using multi‐feature integration framework in complex backgrounds

Cited by 17 publications

References 42 publications

Passive Visual Underwater Surveillance: A Survey

Passive Visual Underwater Surveillance: A Survey

Image Feature Extraction Methods for Structure Detection From Underwater Imagery

Deep Neural Network Driven Automated Underwater Object Detection

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