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Jiang L, Mu Y, Che L, Wu Y. WBi-YOLOSF: improved feature pyramid network for aquatic real-time target detection under the artificial rabbits optimization. Sci Rep 2024; 14:18013. [PMID: 39097637 PMCID: PMC11298005 DOI: 10.1038/s41598-024-68878-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2024] [Accepted: 07/29/2024] [Indexed: 08/05/2024] Open
Abstract
As the pillar industry of coastal areas, aquaculture needs artificial intelligence technology to promote economic development. To realize the automation of the aquaculture industry, this paper proposes a new underwater object detection network: WBi-YOLOSF. It realizes the automatic classification and detection of aquatic products, improves the production efficiency of the aquaculture industry, and promotes its economic development. This paper creates an image dataset containing 15 aquatic products to lay the data foundation for model training. In the data preprocessing part, an underwater image enhancement algorithm is proposed to improve the quality of the data set effectively. Aiming at the problem of high false detection rate and missed detection rate of underwater dense small targets, a new data enhancement method was proposed to improve the training set's data quality comprehensively. Inspired by the weighted bidirectional feature pyramid network, this paper proposes a new feature extraction network: AU-BiFPN, which solves the gradient problem caused by the network hierarchy's deepening on enhancing the network's multi-scale feature fusion. The AU-BiFPN network structure is embedded into the YOLO series network framework, significantly improving the basic network's feature extraction and feature fusion ability and dramatically improving the prediction accuracy without affecting the network inference speed. Here, the swarm intelligence algorithm is introduced to optimize the model hyperparameters, accelerating the convergence speed of model training and significantly reducing the computational cost. At the same time, the model's accuracy is improved by a cliff. In addition, the Funnel Activation is introduced in the network's backbone, and the simple, parameter-free attention module is integrated, effectively improving the accuracy and speed of the model prediction. Ablation and comparison experiments show the effectiveness and superiority of the proposed model. Verified by the mean average precision and frame rate evaluation indicators, the experimental results of the WBi-YOLOSF target detection network can reach 0.982 and 203 frames per second, which are 1.4% and five frames per second higher than the network with the second score. In summary, this method can quickly and accurately identify aquatic products, realize real-time target detection of aquatic products, and lay the foundation for developing an aquaculture automation system.
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Affiliation(s)
- Liubing Jiang
- School of Information and Communication, Guilin University of Electronic Technology, Guilin, 541004, China
- School of Computer and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China
- Key Laboratory of Wireless Broadband Communication and Signal Processing in Guangxi, Guilin University of Electronic Technology, Guilin, 541004, China
| | - Yujie Mu
- School of Computer and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China.
- Key Laboratory of Wireless Broadband Communication and Signal Processing in Guangxi, Guilin University of Electronic Technology, Guilin, 541004, China.
| | - Li Che
- School of Information and Communication, Guilin University of Electronic Technology, Guilin, 541004, China
- School of Computer and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China
| | - Yongman Wu
- School of Computer and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China
- Key Laboratory of Wireless Broadband Communication and Signal Processing in Guangxi, Guilin University of Electronic Technology, Guilin, 541004, China
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Liu M, Li R, Hou M, Zhang C, Hu J, Wu Y. SD-YOLOv8: An Accurate Seriola dumerili Detection Model Based on Improved YOLOv8. SENSORS (BASEL, SWITZERLAND) 2024; 24:3647. [PMID: 38894438 PMCID: PMC11175265 DOI: 10.3390/s24113647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 05/12/2024] [Accepted: 05/30/2024] [Indexed: 06/21/2024]
Abstract
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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Affiliation(s)
- Mingxin Liu
- School of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang 524088, China; (M.L.); (C.Z.); (J.H.)
- Guangdong Provincial Key Laboratory of Intelligent Equipment for South China Sea Marine Ranching, Zhanjiang 524088, China
| | - Ruixin Li
- Naval Architecture and Shipping College, Guangdong Ocean University, Zhanjiang 524088, China; (R.L.); (Y.W.)
| | - Mingxin Hou
- Guangdong Provincial Key Laboratory of Intelligent Equipment for South China Sea Marine Ranching, Zhanjiang 524088, China
- School of Mechanical Engineering, Guangdong Ocean University, Zhanjiang 524088, China
| | - Chun Zhang
- School of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang 524088, China; (M.L.); (C.Z.); (J.H.)
| | - Jiming Hu
- School of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang 524088, China; (M.L.); (C.Z.); (J.H.)
| | - Yujie Wu
- Naval Architecture and Shipping College, Guangdong Ocean University, Zhanjiang 524088, China; (R.L.); (Y.W.)
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Kumar N, Marée R, Geurts P, Muller M. Recent Advances in Bioimage Analysis Methods for Detecting Skeletal Deformities in Biomedical and Aquaculture Fish Species. Biomolecules 2023; 13:1797. [PMID: 38136667 PMCID: PMC10742266 DOI: 10.3390/biom13121797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/05/2023] [Accepted: 12/09/2023] [Indexed: 12/24/2023] Open
Abstract
Detecting skeletal or bone-related deformities in model and aquaculture fish is vital for numerous biomedical studies. In biomedical research, model fish with bone-related disorders are potential indicators of various chemically induced toxins in their environment or poor dietary conditions. In aquaculture, skeletal deformities are affecting fish health, and economic losses are incurred by fish farmers. This survey paper focuses on showcasing the cutting-edge image analysis tools and techniques based on artificial intelligence that are currently applied in the analysis of bone-related deformities in aquaculture and model fish. These methods and tools play a significant role in improving research by automating various aspects of the analysis. This paper also sheds light on some of the hurdles faced when dealing with high-content bioimages and explores potential solutions to overcome these challenges.
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Affiliation(s)
- Navdeep Kumar
- Department of Computer Science and Electrical Engineering, Montefiore Institute, University of Liège, 4000 Liège, Belgium; (R.M.); (P.G.)
| | - Raphaël Marée
- Department of Computer Science and Electrical Engineering, Montefiore Institute, University of Liège, 4000 Liège, Belgium; (R.M.); (P.G.)
| | - Pierre Geurts
- Department of Computer Science and Electrical Engineering, Montefiore Institute, University of Liège, 4000 Liège, Belgium; (R.M.); (P.G.)
| | - Marc Muller
- Laboratory for Organogenesis and Regeneration (LOR), GIGA Institute, University of Liège, 4000 Liège, Belgium;
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Huihui Y, Daoliang L, Yingyi C. A state-of-the-art review of image motion deblurring techniques in precision agriculture. Heliyon 2023; 9:e17332. [PMID: 37416671 PMCID: PMC10320030 DOI: 10.1016/j.heliyon.2023.e17332] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2022] [Revised: 06/07/2023] [Accepted: 06/14/2023] [Indexed: 07/08/2023] Open
Abstract
Image motion deblurring is a crucial technology in computer vision that has gained significant attention attracted by its outstanding ability for accurate acquisition of motion image information, processing and intelligent decision making, etc. Motion blur has recently been considered as one of the major challenges for applications of computer vision in precision agriculture. Motion blurred images seriously affect the accuracy of information acquisition in precision agriculture scene image such as testing, tracking, and behavior analysis of animals, recognition of plant phenotype, critical characteristics of pests and diseases, etc. On the other hand, the fast motion and irregular deformation of agriculture livings, and motion of image capture device all introduce great challenges for image motion deblurring. Hence, the demand of more efficient image motion deblurring method is rapidly increasing and developing in the applications with dynamic scene. Up till now, some studies have been carried out to address this challenge, e.g., spatial motion blur, multi-scale blur and other types of blur. This paper starts with categorization of causes of image blur in precision agriculture. Then, it gives detail introduction of general-purpose motion deblurring methods and their the strengthen and weakness. Furthermore, these methods are compared for the specific applications in precision agriculture e.g., detection and tracking of livestock animal, harvest sorting and grading, and plant disease detection and phenotyping identification etc. Finally, future research directions are discussed to push forward the research and application of advancing in precision agriculture image motion deblurring field.
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Affiliation(s)
- Yu Huihui
- School of Information Science & Technology, Beijing Forestry University, Beijing, 100083, PR China
- National Innovation Center for Digital Fishery, Beijing, 100083, PR China
- Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock, Ministry of Agriculture and Rural Affairs, Beijing, 100083, PR China
| | - Li Daoliang
- National Innovation Center for Digital Fishery, Beijing, 100083, PR China
- Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock, Ministry of Agriculture and Rural Affairs, Beijing, 100083, PR China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, 100083, PR China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, PR China
| | - Chen Yingyi
- National Innovation Center for Digital Fishery, Beijing, 100083, PR China
- Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock, Ministry of Agriculture and Rural Affairs, Beijing, 100083, PR China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, 100083, PR China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, PR China
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MBi-GRUMCONV: A novel Multi Bi-GRU and Multi CNN-Based deep learning model for social media sentiment analysis. JOURNAL OF CLOUD COMPUTING 2023. [DOI: 10.1186/s13677-022-00386-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
AbstractToday, internet and social media is used by many people, both for communication and for expressing opinions about various topics in many domains of life. Various artificial intelligence technologies-based approaches on analysis of these opinions have emerged natural language processing in the name of different tasks. One of these tasks is Sentiment analysis, which is a popular method aiming the task of analyzing people’s opinions which provides a powerful tool in making decisions for people, companies, governments, and researchers. It is desired to investigate the effect of using multi-layered and different neural networks together on the performance of the model to be developed in the sentiment analysis task. In this study, a new, deep learning-based model was proposed for sentiment analysis on IMDB movie reviews dataset. This model performs sentiment classification on vectorized reviews using two methods of Word2Vec, namely, the Skip Gram and Continuous Bag of Words, in three different vector sizes (100, 200, 300), with the help of 6 Bidirectional Gated Recurrent Units and 2 Convolution layers (MBi-GRUMCONV). In the experiments conducted with the proposed model, the dataset was split into 80%-20% and 70%-30% training-test sets, and 10% of the training splits were used for validation purposes. Accuracy and F1 score criteria were used to evaluate the classification performance. The 95.34% accuracy of the proposed model has outperformed the studies in the literature. As a result of the experiments, it was found that Skip Gram has a better contribution to classification success.
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