Underwater Animal Detection Using YOLOV4

Rosli, Mohamed Syazwan Asyraf Bin; Isa, Iza Sazanita; Maruzuki, Mohd Ikmal Fitri; Sulaiman, Siti Noraini; Ahmad, Ibrahim Sayed

doi:10.1109/iccsce52189.2021.9530877

Cited by 16 publications

(2 citation statements)

References 17 publications

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“…Petso et al [29] improved the YOLO v3 and v4 models for individual and group detection by capturing wildlife images from drones at different altitudes. Furthermore, Rosli et al [30] proposed YOLOv4 based on a deep learning algorithm for efficient and accurate underwater detection to overcome the aquatic environment's turbidity, dynamic background, and low visibility and improve the underwater vision system. Yue X et al [31] studied the fast and effective detection of fabric defects and proposed an improved YOLOv4 target detection algorithm.…”

Section: B Yolo Identification Methodsmentioning

confidence: 99%

Open Pose Mask R-CNN Network for Individual Cattle Recognition

Wang,

Zhang,

Gao

et al. 2023

IEEE Access

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Cattle's individual identification plays a crucial role in effectively managing large farms. To enhance agricultural efficiency, promote the digital transformation of animal husbandry, and improve animal welfare, it is essential to employ advanced identification technologies capable of real-time monitoring of cattle individual. This paper introduces a novel network called Open Pose Mask R-CNN (OP-Mask R-CNN) for individual cattle identification, which combines Open Pose with the Mask R-CNN network. Three key strategies are presented to improve the identification of individual cattle. First, optimize the number of convolutional layers in the Mask R-CNN backbone network, i.e., ResNet101. Second, introduce an Open Pose-based bovine skeleton feature extraction method. Finally, construct a fusion mechanism that combines the attention module, the convolutional block attention module (CBAM), the open pose module, and the ResNet101. Experimental results demonstrate that our proposed method achieves a 5.6% increase in recognition accuracy and improves recognition speed compared to the original Mask R-CNN model. This work strikes a balance between accuracy and complexity, facilitating the development of a lightweight bovine individual recognition technique.

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Section: B Yolo Identification Methodsmentioning

confidence: 99%

Open Pose Mask R-CNN Network for Individual Cattle Recognition

Wang,

Zhang,

Gao

et al. 2023

IEEE Access

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“…Underwater target (object) recognition has a great variety of research purposes: monitoring underwater life sustainability, underwater gas pipeline leak detection as an industrial or environmental prevention application, identifying the presence of manufactured archeological objects for archeological research, underwater detection for mineral exploration, among others [1][2][3][4]. In addition, military activities such as Mine Counter Measures (MCM) [5] and Search and Rescue (SAR) operations [6] may also take advantage of this capability.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Layer Perceptron Model for Underwater Object Recognition

Silveira¹,

Monteiro²,

Rocha³

et al. 2023

JBTH

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The more human gets interested in sea exploration, the more research to detect objects under the water is done. Therefore, the ability to detect, classify, recognize and track all kinds of objects is evolving daily. This paper aims to introduce a computer model for underwater object classification and recognition based on a Multilayer Perceptron network. The model was constructed with a mixed dataset for the training phase, combining artificial and natural objects, and it reached approximately 99.97% classifying accuracy.

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Projector deep feature extraction-based garbage image classification model using underwater images

Demir,

Yaman

2024

Multimed Tools Appl

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Marine and ocean pollution is one of the most serious environmental problems in the world. Marine plastics pose a significant threat to the marine ecosystem due to their negative effects. After passing through various processes, plastic waste accumulates on the seafloor and fragments into very small pieces known as microplastics. These microplastics are to blame for the extinction and death of aquatic life. This study obtained a hybrid underwater dataset containing 13,089 images, sized 300 × 300, including garbage and sea animals. In the proposed method, this dataset is used to develop our example projector deep feature generator. In this study, using the Resnet101 network in a sample projector build, the feature generator creates 6,000 features. Using NCA (Neighborhood Component Analysis), the best 1000 features from a pool of 6,000 are selected. The kNN (k-nearest neighbor) algorithm is then used to classify the resulting feature vectors. As validation techniques, both tenfold cross-validations were used. The hybrid dataset's best accuracy was calculated to be 99.35%. Our recommendation is successful based on the comparisons and calculated performance measures.

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Underwater Animal Detection Using YOLOV4

Cited by 16 publications

References 17 publications

Open Pose Mask R-CNN Network for Individual Cattle Recognition

Open Pose Mask R-CNN Network for Individual Cattle Recognition

A Multi-Layer Perceptron Model for Underwater Object Recognition

Projector deep feature extraction-based garbage image classification model using underwater images

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