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A Pyramidal Feature Fusion Model on Swimming Crab Portunus trituberculatus Re-identification
by
Zhang, Kejie
, Shi, Ce
, Ren, Zhiming
, Xie, Zhijun
, Xin, Yu
in
Artificial intelligence
/ Carapace
/ Coastal zone
/ Datasets
/ Deep learning
/ Exoskeleton
/ Experiments
/ Identification
/ individual recognition
/ Internet of Things
/ Marine crustaceans
/ Neural networks
/ Occlusion
/ Portunus trituberculatus
/ Radio frequency identification
/ re-identification
/ Swimming
/ swimming crab
/ Texture
/ triplet loss
2022
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A Pyramidal Feature Fusion Model on Swimming Crab Portunus trituberculatus Re-identification
by
Zhang, Kejie
, Shi, Ce
, Ren, Zhiming
, Xie, Zhijun
, Xin, Yu
in
Artificial intelligence
/ Carapace
/ Coastal zone
/ Datasets
/ Deep learning
/ Exoskeleton
/ Experiments
/ Identification
/ individual recognition
/ Internet of Things
/ Marine crustaceans
/ Neural networks
/ Occlusion
/ Portunus trituberculatus
/ Radio frequency identification
/ re-identification
/ Swimming
/ swimming crab
/ Texture
/ triplet loss
2022
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A Pyramidal Feature Fusion Model on Swimming Crab Portunus trituberculatus Re-identification
by
Zhang, Kejie
, Shi, Ce
, Ren, Zhiming
, Xie, Zhijun
, Xin, Yu
in
Artificial intelligence
/ Carapace
/ Coastal zone
/ Datasets
/ Deep learning
/ Exoskeleton
/ Experiments
/ Identification
/ individual recognition
/ Internet of Things
/ Marine crustaceans
/ Neural networks
/ Occlusion
/ Portunus trituberculatus
/ Radio frequency identification
/ re-identification
/ Swimming
/ swimming crab
/ Texture
/ triplet loss
2022
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A Pyramidal Feature Fusion Model on Swimming Crab Portunus trituberculatus Re-identification
Journal Article
A Pyramidal Feature Fusion Model on Swimming Crab Portunus trituberculatus Re-identification
2022
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Overview
Swimming crab
Portunus trituberculatus
is a vital crab species in coastal areas of China. In this study, an individual re-identification method based on Pyramidal Feature Fusion Model (PFFM) for
P. trituberculatus
was proposed. This method took the carapace texture of
P. trituberculatus
as a “biological fingerprint” and extracted carapace texture features, including global features and local features, to identify
P. trituberculatus
. Furthermore, this method utilized a weight adaptive module to improve re-identification (ReID) accuracy for the
P. trituberculatus
individuals with the incomplete carapace. To strengthen the discrimination of the extracted features, triplet loss was adopted in the model training process to improve the effectiveness of
P. trituberculatus
ReID. Furthermore, three experiments, i.e., PFFM on the effect of pyramidal model,
P. trituberculatus
features analysis, and comparisons to the State-of-the-Arts, were carried out to evaluate PFFM performance. The results showed that the mean average precision (mAP) and Rank-1 values of the proposed method reached 93.2 and 93% in the left half occlusion case, and mAP and Rank-1 values reached 71.8 and 75.4% in the upper half occlusion case. By using the experiments, the effectiveness and robustness of the proposed method were verified.
Publisher
Frontiers Research Foundation,Frontiers Media S.A
Subject
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