Efficient dorsal fin-based classification of Risso's and common Bottlenose dolphins using YOLOv7 and YOLOv8 models for real-time applications
Journal: International Journal of Advanced Technology and Engineering Exploration (IJATEE) (Vol.11, No. 115)Publication Date: 2024-06-29
Authors : Fawaghy Alhashmi Maryam Alhefeiti Shaher Bano Mirza; Fouad Lamghari Ridouane;
Page : 875-887
Keywords : Rissos dolphins; YOLOv7; YOLOv8; Marine; Common bottlenose dolphins.;
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.
Other Latest Articles
- Smart storage strategies for blockchain: a review of approaches
- Efficacy of six Croatian inert dusts and diatomaceous earth Celatom Mn-51® against red flour beetle Tribolium castaneum Herbst (Coleoptera: Tenebrionidae) on wheat
- ASA-LSTM-based brain tumor segmentation and classification in MRI images
- Comparative analysis of potato blight diseases BARI-72 and BARI-73 using a simplified convolutional neural network method
- UNLOCKING HOPE: OVERCOMING THERAPEUTIC CHALLENGES IN PROGRESSIVE MULTIPLE SCLEROSIS
Last modified: 2024-07-04 18:00:21