Machine learning and deep learning strategies to identify Posidonia meadows in underwater images

González-Cid, Yolanda; Burguera, Antoni; Bonin‐Font, Francisco; Matamoros, Alejandro

doi:10.1109/oceanse.2017.8084991

Cited by 20 publications

(18 citation statements)

References 8 publications

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“…Pizarro et al [35] proposed bag of features object recognition system based maximum likelihood classifier (MLC). Burguera et al [36] and Conzalez-Cid et al [37] performed underwater object classification with support vector machine (SVM). However, all these algorithms are shallow intelligent and too dependent on handcrafted features.…”

Section: Related Workmentioning

confidence: 99%

A Real-Time FPGA Accelerator Based on Winograd Algorithm for Underwater Object Detection

Cai

Wang

2021

Electronics

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Real-time object detection is a challenging but crucial task for autonomous underwater vehicles because of the complex underwater imaging environment. Resulted by suspended particles scattering and wavelength-dependent light attenuation, underwater images are always hazy and color-distorted. To overcome the difficulties caused by these problems to underwater object detection, an end-to-end CNN network combined U-Net and MobileNetV3-SSDLite is proposed. Furthermore, the FPGA implementation of various convolution in the proposed network is optimized based on the Winograd algorithm. An efficient upsampling engine is presented, and the FPGA implementation of squeeze-and-excitation module in MobileNetV3 is optimized. The accelerator is implemented on a Zynq XC7Z045 device running at 150 MHz and achieves 23.68 frames per second (fps) and 33.14 fps when using MobileNetV3-Large and MobileNetV3-Small as the feature extractor. Compared to CPU, our accelerator achieves 7.5×–8.7× speedup and 52×–60× energy efficiency.

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Section: Related Workmentioning

confidence: 99%

A Real-Time FPGA Accelerator Based on Winograd Algorithm for Underwater Object Detection

Cai

Wang

2021

Electronics

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“…Beuren et al [28] proposed a whiteness-and blueness-based SVM method. Deep learning technology also produces an increasingly significant role in environmental remote sensing as "big data" [29,30]. Tighe and Lazebnik [10] proposed a non-parametric method combined with Markov Random Field (MRF) to segment sky pixels simply and efficiently.…”

Section: Introductionmentioning

confidence: 99%

Sky and Ground Segmentation in the Navigation Visions of the Planetary Rovers

Kuang

Rana

Zhao

2021

Sensors

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Sky and ground are two essential semantic components in computer vision, robotics, and remote sensing. The sky and ground segmentation has become increasingly popular. This research proposes a sky and ground segmentation framework for the rover navigation visions by adopting weak supervision and transfer learning technologies. A new sky and ground segmentation neural network (network in U-shaped network (NI-U-Net)) and a conservative annotation method have been proposed. The pre-trained process achieves the best results on a popular open benchmark (the Skyfinder dataset) by evaluating seven metrics compared to the state-of-the-art. These seven metrics achieve 99.232%, 99.211%, 99.221%, 99.104%, 0.0077, 0.0427, and 98.223% on accuracy, precision, recall, dice score (F1), misclassification rate (MCR), root mean squared error (RMSE), and intersection over union (IoU), respectively. The conservative annotation method achieves superior performance with limited manual intervention. The NI-U-Net can operate with 40 frames per second (FPS) to maintain the real-time property. The proposed framework successfully fills the gap between the laboratory results (with rich idea data) and the practical application (in the wild). The achievement can provide essential semantic information (sky and ground) for the rover navigation vision.

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“…The step to detect and monitor the seagrass can be through various types of images, including spectral images-based satellites [4], acoustic images [5], underwater video images [6], and underwater digital images [7]. Researchers of marine ecology use underwater digital images for real-time monitoring of the distribution, health, and percent of seagrass coverage [8].…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Enhalus acoroides seagrass from underwater images using the Mask R-CNN method

Pamungkas

Jaya

Iqbal

2021

IOP Conf. Ser.: Earth Environ. Sci.

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Seagrass is a Spermatophyta plant that has many roles, including as a primary producer in the food chain in the waters. Monitoring of seagrass meadows and conditions needs to be done in order to achieve a healthy marine ecosystem. The steps taken in monitoring seagrass are by detecting and segmenting it. The purpose of the study is to implement and get information about the performance of the Mask R-CNN algorithm in detecting and segmenting the Enhalus acoroides. The dataset consists of 500 Enhalus acoroides images that had gone through a color correction and labelling process. The training process was performed with the configuration of 0.001 learning rate, batch size of 4 and some image augmentation was used to avoid overfitting. The optimum weight value was obtained after conducting the learning process with 100 epochs. A confusion matrix was used to evaluate detection performance, and linear regression was used to evaluate the segmentation produced by the model. The model evaluation results showed an accuracy value of 0.9246, a precision value of 0.9507, a recall value of 0.9712 and a correlation coefficient value of 0.8771. The value indicates that the model can detect and segment the seagrass Enhalus acoroides well and accurately.

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Machine learning and deep learning strategies to identify Posidonia meadows in underwater images

Cited by 20 publications

References 8 publications

A Real-Time FPGA Accelerator Based on Winograd Algorithm for Underwater Object Detection

A Real-Time FPGA Accelerator Based on Winograd Algorithm for Underwater Object Detection

Sky and Ground Segmentation in the Navigation Visions of the Planetary Rovers

Segmentation of Enhalus acoroides seagrass from underwater images using the Mask R-CNN method

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