Abstract

The existence of whales and dolphins serves as a key sign of the well-being of the marine environment of that area. It is imperative to undertake research and conservation initiatives to safeguard these marine mammals and their ecosystem. This guarantees their persistence for the well-being of future generations. In recent years, marine surveys conducted in Fujairah offshore waters have generated valuable data concerning the distribution of cetacean species. Notably, common Bottlenose dolphins (Tursiops truncatus) and Risso's dolphins (Grampus griseus) have emerged as prevalent species in the region. These data hold significant information that is useful in species identification and its habitat loss mitigation efforts. Computer vision offers an efficient solution for analysing and interpreting vast visual data compared to of the manual detection methods. Therefore, the primary objective of this study is to assess and contrast the efficacy of you only look once version 7 (YOLOv7) and you only look once version 8 (YOLOv8) models in the identification of cetacean species. The findings indicate that both models exhibit strong performance in identifying and categorizing the desired species. Specifically, YOLOv8 demonstrates a slightly superior precision rate of 91.6% compared to YOLOv7. Additionally, YOLOv8 exhibits improved recall (92.5%) and mean average precision (mAP) of 95.9%. The improved performance of YOLOv8 can be attributed to its comprehensive feature map and optimised convolutional network, combined with a novel backbone network.

Keyword

Risso's dolphins, YOLOv7, YOLOv8, Marine, Common bottlenose dolphins.

Cite this article

Alhashmi F, Alhefeiti M, Mirza SB, Ridouane FL.Efficient dorsal fin-based classification of Risso's and common Bottlenose dolphins using YOLOv7 and YOLOv8 models for real-time applications. International Journal of Advanced Technology and Engineering Exploration. 2024;11(115):875-887. DOI:10.19101/IJATEE.2023.10102512

Refference

[1]Baldwin R, Willson A, Looker E, Buzás B. Growing knowledge of cetacean fauna in the Emirate of Fujairah, UAE. Tribulus. 2018; 26:32-42.

[2]Herrera I, Carrillo M, Cosme DEM, Haroun R. Distribution of cetaceans in the Canary Islands (Northeast Atlantic ocean): implications for the natura 2000 network and future conservation measures. Frontiers in Marine Science. 2021; 8:669790.

[3]Scott MD, Chivers SJ. Distribution and herd structure of bottlenose dolphins in the eastern tropical Pacific Ocean. The Bottlenose Dolphin. 1990:387-402.

[4]Bearzi M. Dolphin sympatric ecology. Marine Biology Research. 2005; 1(3):165-75.

[5]Maze-foley K, Mullin KD. Cetaceans of the oceanic northern Gulf of Mexico: distributions, group sizes and interspecific associations. Journal of Cetacean Research and Management. 2006; 8(2):203-13.

[6]Weir CR. Distribution and seasonality of cetaceans in tropical waters between Angola and the Gulf of Guinea. African Journal of Marine Science. 2011; 33(1):1-5.

[7]Bacon CE, Smultea MA, Fertl D, Würsig B, Burgess EA, Hawks-johnson S. Mixed species associations of marine mammals in the Southern California Bight, with emphasis on Risso’s dolphins (Grampus Griseus). Aquatic Mammals. 2017; 43(2):177-84.

[8]Wells RS, Scott MD. Bottlenose dolphin, Tursiops truncatus, common bottlenose dolphin. In Encyclopedia of Marine Mammals 2018 (pp. 118-25). Academic Press.

[9]Perrin WF, Würsig B, Thewissen JG. Encyclopedia of marine mammals. Academic Press; 2009.

[10]Hartman KL. Rissos dolphin: Grampus Griseus. In encyclopedia of marine mammals 2018 (pp. 824-7). Academic Press.

[11]Hong HHH, Yang XYX, You ZYZ, Cheng FCF. Visual quality detection of aquatic products using machine vision. Aquacultural Engineering. 2014; 63:62-71.

[12]Gümüş B, Balaban MÖ, Ünlüsayin M. Machine vision applications to aquatic foods: a review. Turkish Journal of Fisheries and Aquatic Sciences. 2011; 11(1).

[13]Norouzzadeh MS, Nguyen A, Kosmala M, Swanson A, Palmer MS, Packer C, et al. Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Proceedings of the National Academy of Sciences. 2018 ;115(25): E5716-25.

[14]Willi M, Pitman RT, Cardoso AW, Locke C, Swanson A, Boyer A, et al. Identifying animal species in camera trap images using deep learning and citizen science. Methods in Ecology and Evolution. 2019; 10(1):80-91.

[15]Tabak MA, Norouzzadeh MS, Wolfson DW, Sweeney SJ, Vercauteren KC, Snow NP, et al. Machine learning to classify animal species in camera trap images: applications in ecology. Methods in Ecology and Evolution. 2019; 10(4):585-90.

[16]Kuhlane L, Brown D, Brown A. Exploring the incremental improvements of YOLOv7 on bull sharks in Mozambique. Master Thesis, Rhodes University, Grahamstown, South Africa. 2023

[17]Upulie HD, Kuganandamurthy L. Real-time object detection using YOLO: a review. real-time object detection using YOLO: a review. Academia.edu 2021 (pp. 1-6).

[18]Wang CY, Bochkovskiy A, Liao HY. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In proceedings of the IEEE/CVF conference on computer vision and pattern recognition 2023 (pp. 7464-75). IEEE.

[19]Lou H, Duan X, Guo J, Liu H, Gu J, Bi L, et al. DC-YOLOv8: small-size object detection algorithm based on camera sensor. Electronics. 2023; 12(10):1-14.

[20]Richards VP, Greig TW, Fair PA, Mcculloch SD, Politz C, Natoli A, et al. Patterns of population structure for inshore bottlenose dolphins along the eastern United States. Journal of Heredity. 2013; 104(6):765-78.

[21]Wells RS, Rhinehart HL, Hansen LJ, Sweeney JC, Townsend FI, Stone R, et al. Bottlenose dolphins as marine ecosystem sentinels: developing a health monitoring system. EcoHealth. 2004; 1:246-54.

[22]Natoli A, Al HS. Marine mammals of the emirates: whales, dolphins, porpoises and dugongs. In a natural history of the emirates 2023 (pp. 557-84). Cham: Springer Nature Switzerland.

[23]Diamant R, Testolin A, Shachar I, Galili O, Scheinin A. Observational study on the non-linear response of dolphins to the presence of vessels. Scientific Reports. 2024; 14(1):6062.

[24]Berta A. Return to the sea: the life and evolutionary times of marine mammals. University of California Press; 2020.

[25]Elena F, Ascheri D, Giulia BC, Francesca S, Mcginty N. Bottlenose dolphin (Tursiops truncatus) habitat partitioning in relation to age classes in the northwest Mediterranean Sea. Marine Biology. 2024; 171(7):137.

[26]Nepovinnykh E, Chelak I, Eerola T, Immonen V, Kälviäinen H, Kholiavchenko M, et al. Species-agnostic patterned animal re-identification by aggregating deep local features. International Journal of Computer Vision. 2024:1-6.

[27]Chetoui M, Akhloufi MA. Fire and smoke detection using fine-tuned YOLOv8 and YOLOv7 deep models. Fire. 2024; 7(4):135.

[28]Al MA, Hasan F, Emon MF, Haque MR, Anwary AR, Shatabda S. YOLO-fish: a robust fish detection model to detect fish in realistic underwater environment. Ecological Informatics. 2022; 72:101847.

[29]Asyraf MS, Isa IS, Marzuki MI, Sulaiman SN, Hung CC. CNN-based YOLOv3 comparison for underwater object detection. Journal of Electrical and Electronic Systems Research. 2021; 18:30-7.

[30]Wang M, Liu M, Zhang F, Lei G, Guo J, Wang L. Fast classification and detection of fish images with YOLOv2. In OCEANS-MTS/IEEE kobe techno-oceans (OTO) 2018 (pp. 1-4). IEEE.

[31]Alhmoudi SA, Aldhanhani HR, Ridouane FL, Ateeg M, Al MA, Mirza SB. Study the impact of the anthropogenic activities on the marine environment of Fujairah offshore waters of UAE based on baseline surveys and buoy data. Journal of Marine Sciences. 2024; 2024(1):1998158.