Underwater Fish Segmentation Algorithm Based on Improved PSPNet Network

Han, Yanling; Zheng, Bowen; Kong, Xianghong; Huang, Junjie; Wang, Xiaotong; Ding, Tianhong; Chen, Jiaqi

doi:10.3390/s23198072

Cited by 3 publications

(1 citation statement)

References 22 publications

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“…Ieee Access 11:122911-122925. https://doi.org/10.1109/access.2023.3328940) integrated the Shuffle attention mechanism into YOLOv5s, thereby improving the rapid detection of fish targets in underwater blurry scenes. Han et al (Han YL, Zheng BW, Kong XH et al (2023) Underwater Fish Segmentation Algorithm Based on Improved PSPNet Network. Sensors 23. https://doi.org/10.3390/s23198072) introduced Triple Attention to PSPNet's pyramid module, facilitating cross-dimensional interaction between spatial information to focus on specific positions of fish body features within channels and enhancing clarity around fish edge positions.Jiang et al (Jiang T, Zhou J, Xie BB et al (2024) Improved YOLOv8 Model for Lightweight Pigeon Egg Detection.…”

Section: Introductionmentioning

confidence: 99%

A lightweight fine-grained recognition algorithm based on object detection

Ren,

Shi,

Chen

et al. 2024

Preprint

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In order to enhance the fine-grained recognition of fish species, this paper proposes a lightweight object detection model YOLOv8n-DFG. The model accurately identifies six deep-sea fish species including Flatfin sailfish, Striped marlin, Atlantic bluefin tuna, Bigeye tuna, Longtail tuna, and Yellowfin tuna, meeting the requirements for real-time detection and fishing monitoring. Firstly, by introducing FasterNetBlock and EMA attention mechanism into the YOLOv8 network structure to improve C2f and obtain the C2f-FE module, this model enhances feature extraction accuracy and operational efficiency. Subsequently, it combines BiFPN structure with C2f-FE module to construct a fast and lightweight neck network structure that achieves multi-scale feature fusion. Additionally, Dysample dynamic upsampling module is introduced along with porting of Adown downsampling module from YOLOv9 to optimize feature pyramid sampling method named as YOLOv8-FG. Finally using large-sized YOLOv8s-FG as teacher network and small-sized YOLOv8n-FG as student network based on CWD loss intermediate layer feature distillation method constructs the final model YOLOv8n-DFG. Experimental results on a dataset containing six morphologically similar fish species demonstrate the effectiveness of these improvements and distillation effects are significant. Compared to YOLOv8n, precision has increased by 7.8%, recall by 3.3%, mAP@50 by 5.6%, while FlOPs decreased by 42% with a reduction in model size of 58%. The results indicate that our proposed YOLOv8n-DFG demonstrates exceptional accuracy and real-time performance, effectively fulfilling the requirements for real-time fine-grained fish recognition.

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Section: Introductionmentioning

confidence: 99%

A lightweight fine-grained recognition algorithm based on object detection

Ren,

Shi,

Chen

et al. 2024

Preprint

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A lightweight underwater fish image semantic segmentation model based on U‐Net

Zhang,

Li,

Seet

2024

IET Image Processing

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Semantic segmentation of underwater fish images is vital for monitoring fish stocks, assessing marine resources, and sustaining fisheries. To tackle challenges such as low segmentation accuracy, inadequate real‐time performance, and imprecise location segmentation in current methods, a novel lightweight U‐Net model is proposed. The proposed model acquires more segmentation details by applying a multiple‐input approach at the first four encoder levels. To achieve both lightweight and high accuracy, a multi‐scale residual structure (MRS) module is proposed to reduce parameters and compensate for the accuracy loss caused by the reduction of channels. To improve segmentation accuracy, a multi‐scale skip connection (MSC) structure is further proposed, and the convolution block attention mechanism (CBAM) is introduced at the end of each decoder level for weight adjustment. Experimental results demonstrate a notable reduction in model volume, parameters, and floating‐point operations by 94.20%, 94.39%, and 51.52% respectively, compared to the original model. The proposed model achieves a high mean intersection over union (mIOU) of 94.44%, mean pixel accuracy (mPA) of 97.03%, and a frame rate of 43.62 frames per second (FPS). With its high precision and minimal parameters, the model strikes a balance between accuracy and speed, making it particularly suitable for underwater image segmentation.

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Computer Vision Method for Automatic Detection of Microstructure Defects of Concrete

Beskopylny,

Stel’makh,

Shcherban’

et al. 2024

Sensors

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The search for structural and microstructural defects using simple human vision is associated with significant errors in determining voids, large pores, and violations of the integrity and compactness of particle packing in the micro- and macrostructure of concrete. Computer vision methods, in particular convolutional neural networks, have proven to be reliable tools for the automatic detection of defects during visual inspection of building structures. The study’s objective is to create and compare computer vision algorithms that use convolutional neural networks to identify and analyze damaged sections in concrete samples from different structures. Networks of the following architectures were selected for operation: U-Net, LinkNet, and PSPNet. The analyzed images are photos of concrete samples obtained by laboratory tests to assess the quality in terms of the defection of the integrity and compactness of the structure. During the implementation process, changes in quality metrics such as macro-averaged precision, recall, and F1-score, as well as IoU (Jaccard coefficient) and accuracy, were monitored. The best metrics were demonstrated by the U-Net model, supplemented by the cellular automaton algorithm: precision = 0.91, recall = 0.90, F1 = 0.91, IoU = 0.84, and accuracy = 0.90. The developed segmentation algorithms are universal and show a high quality in highlighting areas of interest under any shooting conditions and different volumes of defective zones, regardless of their localization. The automatization of the process of calculating the damage area and a recommendation in the “critical/uncritical” format can be used to assess the condition of concrete of various types of structures, adjust the formulation, and change the technological parameters of production.

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Underwater Fish Segmentation Algorithm Based on Improved PSPNet Network

Cited by 3 publications

References 22 publications

A lightweight fine-grained recognition algorithm based on object detection

A lightweight fine-grained recognition algorithm based on object detection

A lightweight underwater fish image semantic segmentation model based on U‐Net

Computer Vision Method for Automatic Detection of Microstructure Defects of Concrete

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