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RETRACTED ARTICLE: Underwater image segmentation based on computer vision and research on recognition algorithm

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This article was retracted on 30 December 2021

An Editorial Expression of Concern to this article was published on 28 September 2021

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Abstract

Due to the continuous growth of the world’s population, the development and utilization of marine resources have received great attention. At present, marine fishing relies heavily on divers for underwater operations, which have the disadvantages of high risk, low efficiency, and high cost. Therefore, the development of underwater capture robot which can automatically detect, locate, and capture targets is of great significance for the development of marine economy. Underwater robot is an indispensable equipment for deep-sea operation and plays an irreplaceable role in the development of the ocean. When autonomous underwater vehicles perform underwater operations, they can use computer vision system to obtain clear underwater images and accurate target category information, which can help the manipulator select different grasping parts for different shapes and categories and improve work efficiency. The current underwater vision technology includes “acoustic vision” and “light vision.” Due to the influence of multichannel effect and blind area, the acoustic vision research in detecting and tracking underwater targets is not deep enough. Compared with the acoustic image processing system, the underwater optical vision system has the advantages of image and video capture and has higher real-time performance, which can aim at the target faster and more conveniently. Underwater vision optical system plays an important leading role in the detailed research of underwater vehicle sensing system, which can further improve the autonomous performance of underwater vehicle. In addition, considering the nature of underwater image imaging, we are developing an underwater image segmentation and recognition system based on image processing.

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References

  • Ahmad S, Ahmad S S, Faisal S et al (2019) Automatic fish detection in underwater videos by a deep neuralnetwork-based hybrid motionlearning system[J]. ICESJ Mar Sci 2019

  • Arnéodo A, Decoster N, Roux SG (2000) A wavelet-based method formultifractal image analysis. I. Methodology and test applications on isotropicand anisotropic random rough surfaces. Europ Phy J B 2000(15):567–600

  • Biney CA, Christopher AB (1991) Trace metal concentrations in fish and sediments from the WIWI: a small urban river in Kumasi, Ghana. Trop Ecol 32(2):197–206

    Google Scholar 

  • Chadha DK (1999) A proposed new diagram for geochemical classification of natural waters and interpretation of chemical data. Hydrogeol J 7(5):431–439

  • Chadha DK (1999) A proposed new diagram for geochemical classification of natural waters and interpretation of chemical data. Hydrogeol J 7(5):431–439

  • Chen Z, Wang H, Xu L et al (2014) Visual-adaptation-mechanism based underwater object extraction[J]. Opt Laser Technol 56:119–130

  • Dai C, Lin M, Wang Z et al (2018) Underwater image enhancement based on bright channel color compensation and fusion [J]. Acta Optica Sinica 38(11):86–95

  • Demirel S, Tuzen M, Saracoglu S, Soylak M (2008) Evaluation of various digestion procedures for trace element contents of some food materials. J Hazard Mater 152(3):1020–1026

  • Drury CF, Voroney RP, Beauchamp EG (1991) Availability of NH4+-N to microorganisms and the soil internal N cycle. Soil Biol Biochem 23(2):165–169

    Article  Google Scholar 

  • Duan J, Bressan M, Dance C et al (2010) Tone-mapping high dynamicrange images by novel histogram adjustment[J]. Pattern Recognition 43(5):1847–1862

    Article  Google Scholar 

  • Edet AE, Offiong OE (2002) Evaluation of water quality pollution indices for heavy metal contamination monitoring. A study case from Akpabuyo-Odukpani area, Lower Cross River Basin (southeastern Nigeria). GeoJournal 57:295–304

    Article  Google Scholar 

  • Ediagbonya TF, Nmema E, Nwachukwu PC, Teniola OD (2015) Identification and quantification of heavy metals, coliforms and anions in water bodies using enrichment factors. J Environ Anal Chem 2:146

    Google Scholar 

  • Fabbri C, Islam M J, Sattar J (2018) Enhancingunderwater imagery using generative adversarial networks[C] 2018 IEEEInternational Conference on Robotics and Automation (ICRA). Brisbane, QLD, Australia: IEEE 2018:7159–7165

  • Girshick R, Donahue J, Darrell T et al (2014) Rich feature hierarchies for accurate object detection and semanticsegmentation[C]. IEEE Conference on Computer Vision & Pattern Recognition.Columbus, OH, USA: IEEE, 2014:580–587

  • Godt J, Scheidig F, Grosse-Siestrup C, Esche V, Brandenburg P, Reich A, Groneberg DA (2006) The toxicity of cadmium and resulting hazards for human health. J Occup Med Toxicol 1(1):1–6

    Article  Google Scholar 

  • He K, Sun J, Tang X et al (2011) Single imagehaze removal using dark channel prior[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence 33(12):2341–2353

    Article  Google Scholar 

  • Huang D, Wang F, Song W, et al (2018) Underwater image enhancement with adaptive histogram stretching under different color models [J]. Chinese J Image Grap 023(005):640–651

  • Huang SW, Jin JY (2008) Status of heavy metals in agricultural soils as affected by different patterns of land use. Environ Monit Assess 139(1):317–327

    Article  Google Scholar 

  • Hummel R (1977) Image enhancement by histogram transformation[J].Computer Graphics and Image Processing 6(2):184–195

  • JahicB, Guelfi N, Ries B (2019) Software engineering for dataset augmentation using generative adversarial networks[C] 10th IEEE International Conference on Software Engineering and Service Science. Beijing, China: IEEE 2019:59–66

  • Jian M, Qi Q, Yu H et al (2019) The extended marine underwater environment database and baseline evaluations[J]. Appl Soft Comput 80:425–437

  • Karim Z (2011) Risk assessment of dissolved trace metals in drinking water of Karachi, Pakistan. Bull Environ Contam Toxicol 86(6):676–678

    Article  Google Scholar 

  • Khalid S, Shahid M, Dumat C, Niazi NK, Bibi I, Gul Bakhat HFS, Abbas G, Murtaza B, Javeed HMR (2017) Influence of groundwater and wastewater irrigation on lead accumulation in soil and vegetables: implications for health risk assessment and phytoremediation. Int J Phytoremed 19(11):1037–1046

  • Konovalov DA, Saleh A, Bradley M et al (2019) Underwater fish detection with weak multi-domain supervision[C]. Int Joint Conference on Neural Networks. Budapest, Hungary: IEEE, 2019:1–8

  • Lei F, Zhu L, Wang X (2018) Research on underwater color imagewith improved multi-scale Retinex color gray scale [J]. Small microcomputersystem 039(001):185–188

    Google Scholar 

  • Li H, Yu B (2003) A new image segmentation algorithm based on new multifractal features. Opt Prec Eng 12(6):627–631

  • Li Q, Wang M (2000) Fast recognition method of fruit defects based on fractal features. Chinese J Ima Grap 5(2):144–148

  • Ma M, Huang L (2019). Improved underwater image restoration algorithm based on dark channel a priori [J]. Modern Computer (Professional Edition), 637(01):82–85

  • Magesh NS, Chandrasekar N, Elango L (2017) Trace element concentrations in the groundwater of the Tamiraparani river basin, South India: insights from human health risk and multivariate statistical techniques. Chemosphere 185:468–479

    Article  Google Scholar 

  • Mahmood A Bennamoun M, An S et al (2016) Coral classification with hybrid feature representations[C]. 2016 IEEE International Conference on Image Processing (ICIP). Phoenix, AZ, USA: IEEE 2016:519–523

  • Muhammad N, Banoori N, Akbar A, Azizullah A, Khan M, Qasim M, Rahman H (2016) Microbial and toxic metal contamination in well drinking water: potential health risk in selected areas of Kohat, Pakistan. Urban Water J 28:1–7

    Google Scholar 

  • Nawab J, Khan S, Ali S, Sher H, Rehman Z, Khan K, Tang J, Ahmad A (2016) Risk assessment of heavy metals and biological contamination in drinking water of Malakand Agency, Northern Pakistan. Environ Monit Assess 188:286. https://doi.org/10.1007/s10661-016-5296-1

    Article  Google Scholar 

  • O’Byrne M, Vikram P, Franck S et al (2018) Semantic segmentation of underwater imagery usingd eep networks trained on synthetic imagery[J].J Mar Sci Eng 6(3):93–102

  • Panda B, Chidambaram S, Thivya C, Thilagavathi R, Tirumalesh K, Devaraj N (2020) An attempt to determine the behavior of metals and their dependent thermodynamic saturation states in the groundwater along mountain front and riparian zone. Environ Earth Sci 79(1):17

    Article  Google Scholar 

  • Papanikolaou NC, Hatzidaki EG, Belivanis S, Tzanakakis GN, Tsatsakis AM (2005) Lead toxicity update. A brief review. Med Sci Monit 11(10):329–336

    Google Scholar 

  • Prasad B, Bose JM (2001) Evaluation of the heavy metal pollution index for surface and spring water near a limestone mining area of the lower Himalayas. Environ Geol 41:183–188

    Article  Google Scholar 

  • Prasanna MV, Praveena SM, Chidambaram S, Nagarajan R, Elayaraja A (2012) Evaluation of water quality pollution for heavy metal contamination monitoring: a case study from Curtin Lake, Miri City, East Malaysia. Environ Earth Sci 67:1987–2001. https://doi.org/10.1007/s12665012-1639-6

    Article  Google Scholar 

  • Puthiyasekar C, Neelakandan MA, Poongothai S (2010) Heavy metal contamination in bore water due to industrial pollution and polluted and non-polluted seawater intrusion in Thoothukudi and Tirunelveli of South Tamil Nadu, India. Bull Environ Contam Toxicol 85:598–601. https://doi.org/10.1007/s00128-010-0152-4

    Article  Google Scholar 

  • Qin H Xiu L, Jian L et al (2016) DeepFish: accurateunderwater live fishrecognition with a deep architecture[J]. Neurocomputing 187:49–58

  • Qiu Z, Yao Y, Zhong M (2019) Underwater sea cucumbers detection basedon pruned SSD[C]. 2019 IEEE 3rdAdvanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC). Chongqing, China: IEEE 2019:738–742

    Google Scholar 

  • Rahman ZU, Jobson DJ, Woodell GA (2011) Investigating the relationshipbetween image enhancement andimage compression in the context of the multi-scale Retinex[J]. J Vis Commun Image Represent 22(3):237–250

  • Rattan RK, Datta SP, Chhonkar PK, Suribabu K, Singh AK (2005) Long-term impact of irrigation with sewage effluents on heavy metal content in soils, crops and groundwater—a case study. Agric Ecosyst Environ 109(3-4):310–322

    Article  Google Scholar 

  • Ravindra K, Mor S (2019) Distribution and health risk assessment of arsenic and selected heavy metals in groundwater of Chandigarh, India. Environ Pollut 250:820–830

    Article  Google Scholar 

  • Ren S, He K, Girshick R, et al (2015) FasterR-CNN: towards real-time object detection with region proposal networks[C]International Conference on Neural Information Processing Systems. IEEE, 2015:1137–1149

  • Salman A, Jalal A, Shafait F et al (2016) Fishspecies classification in unconstrained underwater environments based on deeplearning[J]. Limnol Oceanogr Methods 14(9)

  • Selvam S, Venkatramanan S, Singaraja C (2015) A GIS-based assessment of water quality pollution indices for heavy metal contamination in Tuticorin Corporation, Tamilnadu. India Arab J Geosci 8:10611–10623. https://doi.org/10.1007/s12517-015-1968-3

    Article  Google Scholar 

  • Shi Z, Zhou J, Fu Q (2006). Analysis and Simulation of sea clutter characteristics based on multifractal model. J Syst Simu 8(8):2289–2292

  • Tang Z, Zhou B, Dai X et al(2018) Vision enhancement of underwater vehicle based on improved DCPalgorithm [J]. Robot 040(002):222–230

  • Ukah BU, Egbueri JC, Unigwe CO, Ubido OE (2019) Extent of heavy metals pollution and health risk assessment of groundwater in a densely populated industrial area, Lagos, Nigeria. Int J Energy Water Res 3(4):291–303

    Article  Google Scholar 

  • Wang Y, Fu L, Liu K et al (2015) Stable underwater image segmentation in highquality via MRF model[C] OCEANS 2015 - MTS/IEEE Washington. Washington, DC, USA: IEEE 2015:1–4

    Google Scholar 

  • Xu W, Matzner S (2018) Underwater fish detection using deep learning for water power applications[C] 5th Annual Conf. on Computational Science & Computational Intelligence (CSCI’18). Las Vegas, NV, USA: IEEE 2018:313–318

  • Xu Y, Sun M (2016). Underwater image enhancement method based on convolutional neural network [J]. J Jilin Univ Eng Ed 48(06):272–280

  • Zheng H, Sun X, Zheng B et al (2015) Underwater image segmentation via dark channel prior and multiscalehierarchical decomposition[C] OCEANS2015-Genova. Genoa, Italy: IEEE 2015:1–4

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Correspondence to Ma Wenjuan.

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Responsible Editor: Sheldon Williamson

This article is part of the Topical Collection on Environment and Low Carbon Transportation

This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12517-021-09350-y

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Wenjuan, M., Feng, X. RETRACTED ARTICLE: Underwater image segmentation based on computer vision and research on recognition algorithm. Arab J Geosci 14, 1836 (2021). https://doi.org/10.1007/s12517-021-08081-4

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  • DOI: https://doi.org/10.1007/s12517-021-08081-4

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