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| Jellyfish Detection and Recognition Algorithm Based on Improved Faster R-CNN |
| GAO Mei-jing1,LI Shi-yu2,LIU Ze-hao2,ZHANG Bo-zhi2,BAI Yang2,GUAN Ning2,WANG Ping2,CHANG Qiu-yue2 |
1. College of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100081, China
2. The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, School of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China |
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Abstract 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, ResNet101, 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.
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Received: 21 February 2022
Published: 13 January 2023
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2023, Vol. 44 
