# Underwater Fish Detection and Counting Using Mask Regional Convolutional Neural Network

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

- Capture a set of shrimps images;
- Label the shrimp in each of the images captured;
- Train a ResNet101 backbone by using a default parameter to perform image segmentation;
- Train a ResNet101 backbone by performing image segmentation with the parameter calibration; and
- Use a parameter calibration to train a convolutional network to take the segmented image and output an intermediate estimate of shrimp counting.

- The application of a region-based convolutional network to accurately detect the shrimp; and
- The application of a region-based convolutional network to accurately count the number of shrimp.

## 2. Related Work

^{th}Industrial Revolutionary and COVID-19 pandemic, many easy and intermediate human tasks are systematically transforming into computerized decision tools to reduce production costs and increase production goods while maintaining social distancing among workers. Likewise, the Internet of Things is widely booming and applied across sectors to keep up with the industrial needs and better quality of life. Hence, computer vision has become vital in place of many manual inspection systems. They were beginning from hyperspectral images until nano images such as a satellite in a study by Anahita et al. [1], cell detection by Yazan et al. [2], optical character recognition by Tarik et al. [3], vehicle counting by Abbas et al. [4], and rice diseases diagnosis by Abdullah et al. [5]. Furthermore, machine learning enhancement that can reverse feature engineering, namely deep learning, has attracted many researchers to explore the computer vision research area in place of handcrafted feature engineering. This extraordinary capability of imitating nature activity using a convolutional neural network ignites the implementation of shrimp counting to maximize productivity at the various growth stages.

#### 2.1. Handcrafted Feature Engineering

#### 2.2. Autocrafted Feature Engineering

#### 2.3. Non-Machine Learning-Based

#### 2.4. Machine Learning-Based

#### 2.5. Deep Learning-Based

## 3. Mask R-CNN

## 4. Experimental Results and Analysis

#### 4.1. Building a Dataset

#### 4.2. Training the Model

#### 4.3. Experimental Environment

#### 4.4. Evaluation Index

^{2}. With 20 images as the validation set, the validation results of the improved method are compared with those of other methods. Several symbols are used in the equations, and Table 4 shows the notation table.

^{2}is the comparison of results between the actual number of shrimps and the predicted number of shrimps. The method performed on the actual number with the predicted number is using linear regression. In comparing the actual number and the predicted number of shrimps, the value of R

^{2}is evaluated. From the value of R

^{2}, we know whether the regression line corresponds to the data used or not. Equation (9) shows how to calculate the value of R

^{2}.

^{2}is always between 0% and 100%. The value of R

^{2}of 0% represents a model that does not explain any variations in the response variable around its mean. The mean of the dependent variable predicts the dependent variable and the regression model. The value of R

^{2}of 100% represents a model that explains all the variations in the response variable around its mean. Usually, the more significant the R

^{2}value, the better the regression model fits the observations.

#### 4.5. Experimental Results and Analysis

^{2}of 0.9204 in Figure 14 suggests that the regression line does not fit well over the data, which means the predicted number of shrimps is not similar to the actual number of shrimps.

^{2}of 0.9933 in Figure 17 suggests that the regression line fits nicely over the data, which means the predicted number of the shrimps is similar to the actual number of shrimps.

- i
- The shrimp images were recorded from the top view with the assumption of equal size due to similar shrimp age kept in the container.
- ii
- It can automatically estimate the number of shrimps using computer vision and deep learning.
- iii
- Default Mask R-CNN can be manipulated to effectively segment and count tiny shrimps or objects.
- iv
- The shrimp counting accuracy depreciates as the shrimp density increases or intensifies.
- v
- The shrimp estimation efficacy has a linear proportion when the hyperparameters such as maximum detection instance, learning rate, maximum ground truth instance, RPN threshold value, RPN train anchors per image, the number of steps per epoch, train region of interest per image, validation steps, and weight decay are increasing.
- vi
- The linear regression shows that ${R}^{2}$ increases with better precision after performing hyperparameter manipulation over the default Mask R-CNN.
- vii
- This application can reduce shrimp death risk compared to practicing manual counting.

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Appendix A

BACKBONE | resnet101 |

BACKBONE_STRIDES | [4, 8, 16, 32, 64] |

BATCH_SIZE | 1 |

BBOX_STD_DEV | [0.1 0.1 0.2 0.2] |

COMPUTE_BACKBONE_SHAPE | None |

DETECTION_MAX_INSTANCES | 400 |

DETECTION_MIN_CONFIDENCE | 0.7 |

DETECTION_NMS_THRESHOLD | 0.3 |

FPN_CLASSIF_FC_LAYERS_SIZE | 1024 |

GPU_COUNT | 1 |

GRADIENT_CLIP_NORM | 5.0 |

IMAGES_PER_GPU | 1 |

IMAGE_CHANNEL_COUNT | 3 |

IMAGE_MAX_DIM | 1024 |

IMAGE_META_SIZE | 14 |

IMAGE_MIN_DIM | 800 |

IMAGE_MIN_SCALE | 0 |

IMAGE_RESIZE_MODE | square |

IMAGE_SHAPE | [1024 1024 3] |

LEARNING_MOMENTUM | 0.9 |

LEARNING_RATE | 0.01 |

LOSS_WEIGHTS | {'rpn_class_loss': 1.0, 'rpn_bbox_loss': 1.0, 'mrcnn_class_loss': 1.0, 'mrcnn_bbox_loss': 1.0, 'mrcnn_mask_loss': 1.0} |

MASK_POOL_SIZE | 14 |

MASK_SHAPE | [28, 28] |

MAX_GT_INSTANCES | 400 |

MEAN_PIXEL | [123.7 116.8 103.9] |

MINI_MASK_SHAPE | (56, 56) |

NAME | shrimp |

NUM_CLASSES | 2 |

POOL_SIZE | 7 |

POST_NMS_ROIS_INFERENCE | 1000 |

POST_NMS_ROIS_TRAINING | 2000 |

PRE_NMS_LIMIT | 6000 |

ROI_POSITIVE_RATIO | 0.33 |

RPN_ANCHOR_RATIOS | [0.5, 1, 2] |

RPN_ANCHOR_SCALES | (32, 64, 128, 256, 512) |

RPN_ANCHOR_STRIDE | 1 |

RPN_BBOX_STD_DEV | [0.1 0.1 0.2 0.2] |

RPN_NMS_THRESHOLD | 0.8 |

RPN_TRAIN_ANCHORS_PER_IMAGE | 512 |

STEPS_PER_EPOCH | 100 |

TOP_DOWN_PYRAMID_SIZE | 256 |

TRAIN_BN | False |

TRAIN_ROIS_PER_IMAGE | 300 |

USE_MINI_MASK | True |

USE_RPN_ROIS | True |

VALIDATION_STEPS | 200 |

WEIGHT_DEC | 0.001 |

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**Figure 1.**The challenges of shrimp detection using CNN with various illumination types: (

**a**) low and (

**b**) high contrast, from the side view; (

**c**) low, (

**d**) intermediate, and (

**e**) high density of very low-contrast images due to its transparent color from the top view.

**Figure 9.**(

**left**) Less dense category, (

**middle**) medium dense category, and (

**right**) high dense category.

**Figure 10.**Results of precision and recall for the training dataset for default and improved Mask R-CNN.

**Figure 11.**Results of precision and recall for the testing dataset for default and improved Mask R-CNN.

**Figure 12.**Results of mAP when AP = 0.50 and AP 0.75 for the training dataset for default and improved Mask R-CNN.

**Figure 13.**Results of mAP when AP = 0.50 and AP = 0.75 for the testing dataset after applying default and improved Mask R-CNN.

**Figure 14.**Linear regression between the number of ground truths and the predicted number of shrimps for 100 images in the training dataset for the default Mask R-CNN.

**Figure 15.**Comparison of the results of shrimps by using the (

**a**) default Mask R-CNN model and (

**b**) improved Mask R-CNN model. The images of shrimp by density: row 1, less dense with the predicted number of shrimps of 26 (GT = 26) in (

**a**) and 55 (GT = 55) in (

**b**); row 2, medium dense with the predicted number of shrimps of 83 (GT = 84) in (

**a**) and 97 (GT = 99) in (b); row 3, high dense with the predicted number of shrimps of 100 (GT = 104) in (

**a**) and 213 (GT = 256) in (

**b**).

**Figure 16.**(

**a**) Image of shrimp by density: row 1, less dense with the actual number of shrimps of 46 and predicted number of shrimps of 46; row 2, medium dense with the actual number of 118 and predicted number of 118; row 3, dense with the actual number of 188 and predicted a number of 170. (

**b**) Layer of Resnet101 res2c_out, (

**c**) layer of Resnet101 res3c_out, (

**d**) layer of Resnet101 res4w_out and (

**e**) predicted number of shrimps.

**Figure 17.**Linear regression between the number of ground truths and the predicted number of shrimps for 100 images in the training dataset for the improved Mask R-CNN.

Citation and Year | Key Features of Designed Algorithms/Models (Key Objectives and Performance Metrics) | Advantages (Achieved Performance) | Limitations (Based on the Application-Specific Standard Requirements) | |
---|---|---|---|---|

Non-Machine Learning-Based Algorithms | Alomari et al. [2] | Cell counting using dynamic initialization and number of iterations | Reduce false positive rate, resolve easy and medium-density object image | Unresolved high-density object image |

Abdullah et al. [4] | Car counting using adaptive blob edge analysis | Resolve medium- and low-resolution images | Unsupported to vary low-contrast image type | |

Jingyao et al. [11] | Color depth fusion algorithm using Kinect V2 Sensor | High true-positive crop segmentation rates | Low localization accuracy for flower-shaped vegetation | |

Y.H. Toh et al. [12] | Count fish using blob and pixel size analysis methods | Accuracy escalates as the median reference area by averaging the median area of higher fish test cases | As the number of intersecting fish increases, accuracy drops | |

R.T. Labuguen et al. [13] | Count fish fry using adaptive binarization and time frame average | High accuracy with intermediate density | Accuracy drops below 75% as the number of fry fish exceeds 700 | |

J.N. Fabic et al. [14] | Color histogram, canny edge and Zernike shape analysis to identify fish species (Acanthuridae and Scaridae) on underwater video fish sequences | Able to estimate close to the ground truth value | Overcount of less than 10% due to background elimination | |

Subdurally et al. [15] | Calculate headcount using blob and contour analysis | The acceptable head detection rate | Low-value completeness factor for the contour method, skin pixel may vary from dark to bright pixels, and the threshold value effects if the focal length changes continuously. | |

Machine Learning-Based Algorithms | Meesha et al. [16] | Extract wheat grading into five classes using image thresholding, morphological features, support vector machine, and neural network | SVM outperforms NN with 86.8% and 94.5% accuracy rates | Morphological features are exhaustively relying on the pixel roundness ratio, volume, and area |

Abozar et al. [17] | Pig calculation and monitoring using support vector machine | Outperforms when counting lateral and sternal lying posture of gathered pigs | Open-floor, lying close to the feeders or pen wall distracts calculation performance because of alike colors | |

Deep Learning-Based Algorithms | Sethy et al. [18] | Classifies four types of rice leaf diseases | Extracts deep features from resnet50 and mobilenetv2, typical and small CNN models, respectively, and classify them using the SVM classification model | |

Maryam et al. [19] | Tomato estimation using three parallel layers concatenated into one that improvised the Inception-ResNet-A module | The occurrence number of ripe and half-ripe fruits can easily accumulate | Ignore green fruit counting | |

Weilu et al. [20] | Overcome overlapping detections for pest counting with the introduction of CNN with the Zeiler and Fergus (ZF) model and a region proposal network (RPN) with non-maximum suppression (NMS) | Multi-scale images can reduce the error losses and decrease false positives | ZF Net + RPN is comparable to ResNet-50 and ResNet-100 | |

Zhenglin et al. [21] | Mango plant counting using Kalman filter, Hungarian algorithm, and YOLO | LED lighting and a camera have low implementation costs because they exclude differential global navigation satellite system | Disregards localization within the orchard or tree segmentation | |

Liu et al. [22] | MobileNetV2 model based on convolutional neural network (CNN) and transfer learning | Able to classify seven marine animals using a robot camera | Classification accuracy varies according to the number of individual animals in each captured image | |

Merencilla et al. [23] | Shark EYE used YOLOv3 algorithm for object detection, multiscale prediction, and logistic regression-based bounding box prediction | The system uses a large collection of great white sharks’ datasets underwater for training, as sharks are hard to differentiate from other shark-like animals in an underwater environment | It needs further to be refined for a wearable device equipped with a wide-angle camera | |

Li et al. [24] | Segment marine animals involving conch, fish, and crab with a complex environment, MAS3K datasets | Introduce an enhanced cascade decoder network (ECDNet) with multiple interactive feature enhancement modules (IFEMs) and cascade decoder modules (CDMs) | The single decoder and the influence of the number of CDMs need to improvise for better performance |

Parameter | Default Mask R-CNN | Improved Mask R-CNN |
---|---|---|

Regularization | L2 | L1 |

Maximum Detection instance | 100 | 400 |

Learning rate | 0.001 | 0.01 |

Maximum ground truth instance | 100 | 400 |

Name | NONE | SHRIMP |

RPN threshold value | 0.7 | 0.8 |

RPN train anchors per image | 256 | 512 |

Number of Steps per epoch | 50 | 100 |

Train Region of Interest per image | 200 | 300 |

Validation steps | 50 | 200 |

Weight decay | 0.0001 | 0.001 |

Attribute Name | Attribute Value |
---|---|

Tensorflow version | 1.3.0 |

Keras Version | 2.0.8 |

RAM | 8 GB |

Processor | Intel (R) Core TM i7-7700HQ CPU @ 2.80GHz |

Graphics | GeForce GTX 1050 |

Operating system version | Windows 10 Pro, 64 bit |

Symbol | Meaning |
---|---|

$TP$ | The number of images consisting of shrimp has been correctly localized |

$FP$ | The number of images unsuccessfully or partially localize the shrimp |

$FN$ | The number of images unsuccessfully localize the shrimp |

$\sum _{q=1}^{Q}}AveP\left(q\right)$ | $\mathrm{The}\mathrm{variable}\mathrm{q}\mathrm{is}\mathrm{the}\mathrm{number}\mathrm{of}\mathrm{queries}\mathrm{in}\mathrm{the}\mathrm{set},\mathrm{and}AveP\left(q\right)$ is the average accuracy average precision for a particular query |

$Q$ | A particular query |

${{\displaystyle \int}}_{0}^{1}p\left(r\right)dr$ | Mean average precision by using all the point interpolation of precision and recall |

$n$ | Number of images in the training dataset |

∑ | Summation |

$x$ | Predicted number of shrimps |

$y$ | Number of ground truths |

Train | Test | |||
---|---|---|---|---|

Precision | Recall | Precision | Recall | |

Mask R-CNN | 95.18% | 48.65% | 95.15% | 50.63% |

Improved Mask R-CNN | 95.79% | 51.77% | 95.30% | 52.20% |

Train | Test | |||
---|---|---|---|---|

mAP (AP_{0.50}) | AP_{0.75} | mAP (AP_{0.50}) | AP_{0.75} | |

Mask R-CNN | 90.23% | 65.85% | 95.83% | 72.77% |

Improved Mask R-CNN | 99.00% | 96.35% | 99.70% | 98.50% |

Category | No. of Ground Truths | No. of Predicted Shrimps | Accuracy Rate | Error Rate |
---|---|---|---|---|

Less dense | 2682 | 2671 | 99.59% | 0.41% |

Medium dense | 1715 | 1679 | 97.90% | 2.10% |

Dense | 644 | 564 | 87.58% | 12.42% |

Total | 5041 | 4914 | 97.48% | 2.52% |

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## Share and Cite

**MDPI and ACS Style**

Hong Khai, T.; Abdullah, S.N.H.S.; Hasan, M.K.; Tarmizi, A.
Underwater Fish Detection and Counting Using Mask Regional Convolutional Neural Network. *Water* **2022**, *14*, 222.
https://doi.org/10.3390/w14020222

**AMA Style**

Hong Khai T, Abdullah SNHS, Hasan MK, Tarmizi A.
Underwater Fish Detection and Counting Using Mask Regional Convolutional Neural Network. *Water*. 2022; 14(2):222.
https://doi.org/10.3390/w14020222

**Chicago/Turabian Style**

Hong Khai, Teh, Siti Norul Huda Sheikh Abdullah, Mohammad Kamrul Hasan, and Ahmad Tarmizi.
2022. "Underwater Fish Detection and Counting Using Mask Regional Convolutional Neural Network" *Water* 14, no. 2: 222.
https://doi.org/10.3390/w14020222