Fish Face Identification Based on Rotated Object Detection: Dataset and Exploration

Li, Danyang; Su, Houcheng; Jiang, Kailin; Liú, Dan; Duan, Xuliang

doi:10.3390/fishes7050219

Cited by 15 publications

(6 citation statements)

References 35 publications

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“…FocalLoss is often used to suppress background classes in unbalanced data and target detection ( Li et al, 2022 ). As can be seen in the results of Table 4 for both Group 2 and 3, and Group 7 and 10, the accuracy of the model after FocalLoss processing decreases with the golden crucian carp dataset and the corresponding settings in this article.…”

Section: Resultsmentioning

confidence: 99%

A multitask model for realtime fish detection and segmentation based on YOLOv5

Liu¹,

Gong²,

Li³

et al. 2023

PeerJ Computer Science

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The accuracy of fish farming and real-time monitoring are essential to the development of “intelligent” fish farming. Although the existing instance segmentation networks (such as Maskrcnn) can detect and segment the fish, most of them are not effective in real-time monitoring. In order to improve the accuracy of fish image segmentation and promote the accurate and intelligent development of fish farming industry, this article uses YOLOv5 as the backbone network and object detection branch, combined with semantic segmentation head for real-time fish detection and segmentation. The experiments show that the object detection precision can reach 95.4% and the semantic segmentation accuracy can reach 98.5% with the algorithm structure proposed in this article, based on the golden crucian carp dataset, and 116.6 FPS can be achieved on RTX3060. On the publicly available dataset PASCAL VOC 2007, the object detection precision is 73.8%, the semantic segmentation accuracy is 84.3%, and the speed is up to 120 FPS on RTX3060.

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Section: Resultsmentioning

confidence: 99%

A multitask model for realtime fish detection and segmentation based on YOLOv5

Liu¹,

Gong²,

Li³

et al. 2023

PeerJ Computer Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…When contrasted with the YOLOv3, YOLOv3-TL, YOLOv3-BL, YOLOv4, YOLOv5, Faster-RCNN, and YOLOv7 models, the FD_Net model’s mAP performance is superior. When compared to other target identification algorithms, the mAP displayed unusually high levels of stability and discrimination [ 102 ]. In addition to this, it offers a single-figure evaluation of quality in comparison to memory levels.…”

Section: Resultsmentioning

confidence: 99%

Multi-classification deep neural networks for identification of fish species using camera captured images

et al. 2023

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Regular monitoring of the number of various fish species in a variety of habitats is essential for marine conservation efforts and marine biology research. To address the shortcomings of existing manual underwater video fish sampling methods, a plethora of computer-based techniques are proposed. However, there is no perfect approach for the automated identification and categorizing of fish species. This is primarily due to the difficulties inherent in capturing underwater videos, such as ambient changes in luminance, fish camouflage, dynamic environments, watercolor, poor resolution, shape variation of moving fish, and tiny differences between certain fish species. This study has proposed a novel Fish Detection Network (FD_Net) for the detection of nine different types of fish species using a camera-captured image that is based on the improved YOLOv7 algorithm by exchanging Darknet53 for MobileNetv3 and depthwise separable convolution for 3 x 3 filter size in the augmented feature extraction network bottleneck attention module (BNAM). The mean average precision (mAP) is 14.29% higher than it was in the initial version of YOLOv7. The network that is utilized in the method for the extraction of features is an improved version of DenseNet-169, and the loss function is an Arcface Loss. Widening the receptive field and improving the capability of feature extraction are achieved by incorporating dilated convolution into the dense block, removing the max-pooling layer from the trunk, and incorporating the BNAM into the dense block of the DenseNet-169 neural network. The results of several experiments comparisons and ablation experiments demonstrate that our proposed FD_Net has a higher detection mAP than YOLOv3, YOLOv3-TL, YOLOv3-BL, YOLOv4, YOLOv5, Faster-RCNN, and the most recent YOLOv7 model, and is more accurate for target fish species detection tasks in complex environments.

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“…Meidell and Sjøblom [16] reports a true positive rate of 96% on 225 thousand images of salmon divided between 715 individuals. Li et al [13] achieved an accuracy of 92% using 3412 images of 10 individuals using their novel FFRNet network. These studies have in common that they were carried out in captivity and are not using temporally indepen-dent observations.…”

Section: Related Workmentioning

confidence: 99%

A contrastive learning approach for individual re-identification in a wild fish population

Olsen

Sørdalen

Goodwin

et al. 2023

nldl

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In both terrestrial and marine ecology, physical tagging is a frequently used method to study population dynamics and behavior. However, such tagging techniques are increasingly being replaced by individual re-identification using image analysis. This paper introduces a contrastive learning-based model for identifying individuals. The model uses the first parts of the Inception v3 network, supported by a projection head, and we use contrastive learning to find similar or dissimilar image pairs from a collection of uniform photographs. We apply this technique for corkwing wrasse, Symphodus melops, an ecologically and commercially important fish species. Photos are taken during repeated catches of the same individuals from a wild population, where the intervals between individual sightings might range from a few days to several years. Our model achieves a one-shot accuracy of 0.35, a 5-shot accuracy of 0.56, and a 100-shot accuracy of 0.88, on our dataset.

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Fish Face Identification Based on Rotated Object Detection: Dataset and Exploration

Cited by 15 publications

References 35 publications

A multitask model for realtime fish detection and segmentation based on YOLOv5

A multitask model for realtime fish detection and segmentation based on YOLOv5

Multi-classification deep neural networks for identification of fish species using camera captured images

A contrastive learning approach for individual re-identification in a wild fish population

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