Looking for an expert’s professionals touch for your Machine Learning projects.

phdservices.org gives scholars the best guidance for all research encounters that you may encounter with. Our team comprises of qualified professionals to assist you in all paper writing services. The trending topics will be suggested by us from latest IEEE papers of the current year. The fish detection using machine learning is a beneficial application in particular fields like aquaculture, marine biology, and environmental monitoring. Some of the thesis ideas are also shared stay inspired by our work.

 Let us build a fish detection system through the given guidelines,

Objective Definition

We create a machine learning model which is portable for identifying fish in images or videos and it is the initial objective of this system.

Data Collection

• Data Source: From underwater cameras, divers, drones or other sources, we fetch the images or video footage. The data are merging of images with fish and without fish for making a binary classification setup. If species-level classification is required, then collect images for each fish pieces.

Data Preprocessing

• Annotation: Around the fish, the images are being tagged with wrapped boxes or label them with their species names. Label box or VGG Image Annotator is involved tools we utilize in this process.
• Image Augmentation: Augmentation methods like contrast adjustments, rotations, brightness, zooming, and flips are used by us for transforming the dataset and ignoring the over fitting.
• Normalization: The pixel values are standardized typically the range as [0, 1].

Model Selection and Development

Let us consider the following object detection models for detecting the fish using fish detection system,

• SSD (Single Shot Multibox Detector )
• YOLO (You Only Look Once )
• Faster R-CNN

The classical image classification model such as CNN (Convolutional Neural Network) is used for detecting the fish species.

Training the Model

• Split the Data: We categorize the dataset into training, validation and test sets.
• Training: By observing the loss and accuracy for some metrics like IoU for detection tasks, we train the model on the training dataset on the validation test.

Model Evaluation

Some generally used metrics which involves Intersection over Union (IoU), accuracy and Mean Average Precision (mAP). To perform classification task the metrics include such as, accuracy, precision, recall, and F1-score.  Our model is examined on the test dataset and observing the symptoms for class imbalance problems or over fitting.

Optimization & Hyper parameter Tuning (if required)

Based on validation set, the model architecture, learning rate, batch size or other hyper parameters are altered by us.

The tools deployed like transfer learning, here the models are pre-trained on a large dataset like ImageNet is making improvements in the fish dataset. If the fish dataset is relatively small in particular dataset, it provides us best results.

Deployment

Once we get satisfied with the model, the model is used on edge devices such as cameras on underwater drones or fishing boats or the servers for batch processing of videos and images.

TensorFlow Lite or ONNX are such frameworks used on edge devices.

User Interface (if applicable)

 We are supposed in making a software tool, then design an UI (User Interface) that permit users for transferring the videos for fish detection or classification. It offers the perfect result which indicates fish with clear vision.

Conclusion & Future Enhancements

Make an outline for our project achievements and challenges faced by them. The developments which enhance our system such as,

• Differentiating between more fish species.
• Estimating the size or age of detected fish.
• Real-time processing for live video streams.

    Tips

In underwater process, lighting and turbidity are might different. It is critical challenge for us in making detection. So, make confirm that our dataset catches this variability.

For utilize such system, then evaluate about some ethical implications particularly for fishing activities. We must establish the system with balanced and responsible for fishing practices.

A machine learning- based fish detection system provides us more beneficial insights and techniques for the collaborators in the advancing field in marine such as, marine conservation and sustainable fishing practices. Many projects are now handled by us under machine learning our experts give scholars a brief description of the research ideas.

Fish Detection using Machine Learning Thesis Topics

There are PhD expert professors’ team under machine learning in our concern to offer best advice. Thesis writing will be well drafted all thesis ideas and thesis topics will be shared from us to guide to in right path as per your interest. We get regular feedbacks from scholars and do thesis editing service if needed.   

1. Effects of image data quality on a convolutional neural network trained in-tank fish detection model for recirculating aquaculture systems

Keywords:

Underwater imaging, Artificial intelligence, Machine learning, RAS, Precision aquaculture

            Our paper led to examine the effects of sensor selection, image quality, data size, imaging conditions and preprocessing operations on ML method to obtain the accuracy for fish detection below RAS manufacture conditions. We have established an imaging platform consist of four off-the-shelf sensors modified for underwater image classification. We have to develop the data acquired from imaging sensor under two light conditions i.e., Ambient and Supplemental. The images improved and trained by utilizing YOLOv5 model. 

2. Automatic Fish Detection in Underwater Videos using Machine Learning

Keywords

YOLO (You Only Look Once), YOLOv5S, YOLOv5M, YOLOv5L, Fish-Detection, Classification

            The goal of our paper is to identify fish in underwater recording and regulate what type of fish they are. We utilized the LCF-15 dataset contains images that were utilized to train and test. We have utilized various methods of YOLOv5 (YOLOv5S, YOLOv5M and YOLOv5L) to train and test our gathered dataset. Our YOLOv5M offers an enhanced detection accuracy.

3. Automating Fish Detection and Species Classification in Underwaters Using Deep Learning Model

Keywords

Deep learning, Convolution neural network (CNN), Underwater, Kaggle, Unmanned

            We utilize the CNN method to automate fish detection and to find the kind of species from underwater image or video. We have to use several machine learning methods to test the detection of species but the size of the data has to improve and ML gives less accuracy. In ML classification methods we have the chance of arising overfitting problem when the data size is long we try to improve the method by utilizing CNN to efficacy of our proposed method.  

4. Fish Detection and Classification for Automatic Sorting System with an Optimized YOLO Algorithm

Keywords

Automatic fish sorting, fish classification, fish recognition, computer and machine vision

            Our paper proposes a method based on the recognition method YOLOv4 that can be optimized with a distinctive labeling method. Our proposed model can be tested with videos of real fish running on a conveyor that can randomly put in one location and can order a speed to get high accuracy. Our study is simple but an effective method can be expected to automatically detect, classify and sorting fish. 

5. Fake Hilsa Fish Detection Using Machine Vision

Keywords

Hilsa fish, Fake Hilsa fish, DenseNet201, Image processing

            We suggest a method which can ready to find original Hilsa fish and fake Hilsa fish. We initially do an investigation on finding original Hilsa fish. We have gathered more images of original and counterfeit Hilsa fish. We have to categorize the images by utilizing some deep learning methods. We have to compare the performance between them. Our DenseNet201 method performs the greater accuracy.

6. Underwater Fish Detection and Classification using Deep Learning

Keywords

Underwater fish recognition, MobileNet model

            We offer a number of underwater computer vision, ML-based automated systems to detect and classify the fish. We utilized MobileNet method to detect and recognize the fish breed in our suggested work. We have to preprocess the dataset as earlier we can execute it to get suitable metrics. We can work based on the kaggle dataset that consist of nine various breeds. With high accuracy we have to detect and recognize nine different breeds.

7. Fish Detection and Classification

Keywords

Support Vector Machine, KNN, ANN

            We used different methods like KNN and SVM to overcome the problem of segmentation error, noise, distortion in image etc. We utilized SVM to find the kind of fish by utilizing four kernel functions namely linear, polynomial, sigmoid, and radial substructure functions. We proposed a SVM based approach to enhance fish species detection and overcome the restriction of different existing method.

8. Temperate fish detection and classification: a deep learning-based approach

Keywords:

Biometric fish classification, temperate species, Object detection, underwater video

            Our paper suggested a two-step deep learning method for detection and classification of temperate fishes without pre-filtering. Initially we have to detect every fish in an image, for this we utilize YOLO object detection method. Next we implement a CNN with Squeeze-and-Excitation (SE) to classify the fish without pre-filtering. To enhance the accuracy of classification we use transfer learning method. Our solution achieves the high accuracy by utilizing the pretrained method. 

9. Composited FishNet: Fish Detection and Species Recognition From Low-Quality Underwater Videos

Keywords:

Composite backbone network, composited FishNet, fish detection, feature fusion

            Our paper proposes a novel fish detection outline based on composite backbone and an improved path aggregation network called Composited FishNet. To enhance the ResNet a new CBresnet can utilize to learn the information of scene change. We also combine high and low feature from CBresnet and the EPANet is also considered to solve the unsatisfactory use of semantic information caused by linear sampling. Our method can be useful for fish detection and finding complex underwater environments.  

10. Detection of fish freshness using artificial intelligence methods

Keywords:

Fish freshness, Transfer learning, Classification, Skin coloration, Fish body

            Our paper suggested a new method to estimate the freshness of fish by utilizing deep learning methods. By utilize the deep learning methods like SqueezeNet and InceptionV3 to categorize the fish according to their freshness utilizing a dataset. We also offer a new method to discourse the challenge. By analyzing the results, we revealed that SVM, ANN and LR can results in greater accuracy for every deep learning method.