TY - JOUR
T1 - 基于改进ＦａｓｔｅｒＲ-ＣＮＮ的水母检测与识别算法
AU - Gao, Mei Jing
AU - Li, Shi Yu
AU - Liu, Ze Hao
AU - Zhang, Bo Zhi
AU - Bai, Yang
AU - Guan, Ning
AU - Wang, Ping
AU - Chang, Qiu Yue
N1 - Publisher Copyright:
© 2023 Chinese Society for Measurement. All rights reserved.
PY - 2023/1
Y1 - 2023/1
N2 - A jellyfish detection algorithm based on improved Faster R-CNN is proposed. Firstly, a data set containing 7 species of jellyfishes is established. Secondly, on the premise of ensuring the accuracy, the number of branches C is set to 8 to solve the problem that ResNeXt ( C = 32) has a high amount of calculation for target detection. Finally, to solve the problems of low detection accuracy and small individuals unable to be recognized, expansion convolution is introduced into the residual network. The experimental results shown that compared with VGG16, ResNetlOl, ResNeXt (C = 32) and ResNeXt ( C = 8 ) , the mAP value of the proposed algorithm increase by 3. 15% , 2. 09% , 3. 01 % and 2. 36% . F1 -score increase by 2. 53% , 1. 99% , 2. 01% and 2. 31% . Loss function convergence value of the proposed algorithm approach to 0. Results of P-R curve, visual analysis and video detection show that the accuracy and detection number of jellyfish by the proposed algorithm is the best, the proposed algorithm has high detection accuracy and can meet the requirements of real¬time monitoring.
AB - A jellyfish detection algorithm based on improved Faster R-CNN is proposed. Firstly, a data set containing 7 species of jellyfishes is established. Secondly, on the premise of ensuring the accuracy, the number of branches C is set to 8 to solve the problem that ResNeXt ( C = 32) has a high amount of calculation for target detection. Finally, to solve the problems of low detection accuracy and small individuals unable to be recognized, expansion convolution is introduced into the residual network. The experimental results shown that compared with VGG16, ResNetlOl, ResNeXt (C = 32) and ResNeXt ( C = 8 ) , the mAP value of the proposed algorithm increase by 3. 15% , 2. 09% , 3. 01 % and 2. 36% . F1 -score increase by 2. 53% , 1. 99% , 2. 01% and 2. 31% . Loss function convergence value of the proposed algorithm approach to 0. Results of P-R curve, visual analysis and video detection show that the accuracy and detection number of jellyfish by the proposed algorithm is the best, the proposed algorithm has high detection accuracy and can meet the requirements of real¬time monitoring.
KW - Faster R-CNN
KW - ResNeXt
KW - expansion convolution
KW - jellyfish detection and recognition
KW - metrology
KW - residual network
UR - http://www.scopus.com/inward/record.url?scp=85160323694&partnerID=8YFLogxK
U2 - 10.3969/j.issn.1000-1158.2023.01.09
DO - 10.3969/j.issn.1000-1158.2023.01.09
M3 - 文章
AN - SCOPUS:85160323694
SN - 1000-1158
VL - 44
SP - 54
EP - 61
JO - Jiliang Xuebao/Acta Metrologica Sinica
JF - Jiliang Xuebao/Acta Metrologica Sinica
IS - 1
ER -