Survival pressure and tolerance of juvenile greater amberjack (Seriola dumerili) under acute hypo- and hyper-salinity stress

Shi, Hongjuan; Li, Jinfeng; Li, Xinyi; Ru, Xiaoying; Huang, Yang; Zhu, Chunhua; Li, Guangli

doi:10.1016/j.aqrep.2024.102150

Cited by 1 publication

(1 citation statement)

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“…Seriola dumerili (SD), commonly known as “Kanpachi”, is a fish species primarily found in the upper layers of the ocean. It inhabits tropical and temperate waters of major ocean regions [ 1 ]. Species of the genus Seriola are targeted for aquaculture due to their fast growth and large size.…”

Section: Introductionmentioning

confidence: 99%

SD-YOLOv8: An Accurate Seriola dumerili Detection Model Based on Improved YOLOv8

Liu,

Li,

Hou

et al. 2024

Sensors

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Accurate identification of Seriola dumerili (SD) offers crucial technical support for aquaculture practices and behavioral research of this species. However, the task of discerning S. dumerili from complex underwater settings, fluctuating light conditions, and schools of fish presents a challenge. This paper proposes an intelligent recognition model based on the YOLOv8 network called SD-YOLOv8. By adding a small object detection layer and head, our model has a positive impact on the recognition capabilities for both close and distant instances of S. dumerili, significantly improving them. We construct a convenient S. dumerili dataset and introduce the deformable convolution network v2 (DCNv2) to enhance the information extraction process. Additionally, we employ the bottleneck attention module (BAM) and redesign the spatial pyramid pooling fusion (SPPF) for multidimensional feature extraction and fusion. The Inner-MPDIoU bounding box regression function adjusts the scale factor and evaluates geometric ratios to improve box positioning accuracy. The experimental results show that our SD-YOLOv8 model achieves higher accuracy and average precision, increasing from 89.2% to 93.2% and from 92.2% to 95.7%, respectively. Overall, our model enhances detection accuracy, providing a reliable foundation for the accurate detection of fishes.

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Section: Introductionmentioning

confidence: 99%