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What type of CNN will be suitable for underwater image processing?
Image Processing: Algorithm Improvement for 'Coca-Cola Can' RecognitionWhat is “semantic segmentation” compared to “segmentation” and “scene labeling”?How do I label image segments generated from segmentation algriothm for a Convolutional Neural Network?Why rotation-invariant neural networks are not used in winners of the popular competitions?Comparison of HoG with CNNCNN parameter estimateReduce training time for cnnReal-time Image Processing: Noise in HSV image (openCV)Getting wrong prediction for cnn (Dogs Vs Cat ) KerasImage regression with CNN
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The primary objective (my assigned work) is to do an image segmentation for the underwater images using a convolutional neural network. The camera shots taken from the underwater structure will have poor image quality due to severe noise and bad light exposure. In order to achieve higher classification accuracy, I want to do an automatic image enhancement for the images (see the attached file). So, I want to know, which CNN architecture will be best to do both tasks. Please kindly suggest any possible solutions to achieve the objective.
See the image attached for reference
python-3.x image-processing computer-vision conv-neural-network image-segmentation
asked Mar 8 at 5:34
oveznovezn
63
add a comment |
The primary objective (my assigned work) is to do an image segmentation for the underwater images using a convolutional neural network. The camera shots taken from the underwater structure will have poor image quality due to severe noise and bad light exposure. In order to achieve higher classification accuracy, I want to do an automatic image enhancement for the images (see the attached file). So, I want to know, which CNN architecture will be best to do both tasks. Please kindly suggest any possible solutions to achieve the objective.
See the image attached for reference
python-3.x image-processing computer-vision conv-neural-network image-segmentation
asked Mar 8 at 5:34
oveznovezn
63
add a comment |
The primary objective (my assigned work) is to do an image segmentation for the underwater images using a convolutional neural network. The camera shots taken from the underwater structure will have poor image quality due to severe noise and bad light exposure. In order to achieve higher classification accuracy, I want to do an automatic image enhancement for the images (see the attached file). So, I want to know, which CNN architecture will be best to do both tasks. Please kindly suggest any possible solutions to achieve the objective.
See the image attached for reference
python-3.x image-processing computer-vision conv-neural-network image-segmentation
asked Mar 8 at 5:34
oveznovezn
63
The primary objective (my assigned work) is to do an image segmentation for the underwater images using a convolutional neural network. The camera shots taken from the underwater structure will have poor image quality due to severe noise and bad light exposure. In order to achieve higher classification accuracy, I want to do an automatic image enhancement for the images (see the attached file). So, I want to know, which CNN architecture will be best to do both tasks. Please kindly suggest any possible solutions to achieve the objective.
See the image attached for reference
python-3.x image-processing computer-vision conv-neural-network image-segmentation
python-3.x image-processing computer-vision conv-neural-network image-segmentation
asked Mar 8 at 5:34
oveznovezn
63
asked Mar 8 at 5:34
oveznovezn
63
asked Mar 8 at 5:34
oveznovezn
63
asked Mar 8 at 5:34
oveznovezn
63
asked Mar 8 at 5:34
oveznovezn
63
63
add a comment |
add a comment |
1 Answer
1
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oldest
votes
What do you need to segment? I'd be nice so see some labels of the segmentation.
You may not need to enhance the image, if all your dataset has that same amount of noise, the network will generalize properly.
Regarding CNNs architectures, it depends on the constraints you have with processing power and accuracy. If that is not a constrain go with something like MaskRCNN, check that repo as a good starting point, some results are like this:
Be mindful it's a bit of a complex architecture so inference times might be a bit too high (but it's doable on realtime depending your gpu).
Other simple architectures are FCN (Fully Convolutional Networks) with are basically your CNN but instead of fully connected layers:
You replace with with Fully Convolutional Layers:
Images taken from HERE.
The advantage of this FCNs are that they are really easy to implement and modify since you can go with simple architectures (FCN-Alexnet), to more complex and more accurate ones (FCN-VGG, FCN-Resnet).
Also, I think you don't mention framework, there are many to choose from and it depends on your familiarly with languages, most of them you can do them with python:
- TensorFlow
- Pytorch
- MXNet
But if you are a beginner, try starting with a GUI based one, Nvidia Digits is a great starting point and really easy to configure, it's based on Caffe so it's fairly fast when deploying and can easily be integrated with accelerators like TensorRT.
answered Mar 8 at 9:22
bpinayabpinaya
176110
Thank you @bpinaya for your detailed reply. My project is to do automatic detection of faults in hull (main body of the ship) using CNN. I want to segment the faulty areas such as broken structures and cracks in the image. We can assume most of the pictures will be having same amount of noises.
– ovezn
Mar 9 at 11:52
I am a beginner to neural networks. The basic knowledge I have is how a neural network and cnn works. I have some hands on in python. As of now i am able to build a neural network for classification and regression by editing already written code according to my needs. So I am not a programming person and from core engineering field who was put incharge of doing a automation work. I can get a basic idea from your reply but not able to understand the full meaning because i am in a beginner level. But I will take this as a thread and try to learn from the hints.
– ovezn
Mar 9 at 11:52
1
In that case you need to start from scratch, I'll suggest you take a course regarding CNNs first, since that'll help you understand what you are coding, the best one out there might be this coursera.org/specializations/deep-learning , but then again it's paid, but it's worth it. If you have a hard deadline try Nvidia Digits (the link I posted), install it and fine tune already trained networks.
– bpinaya
Mar 10 at 17:16
Thank you so much for your valuable suggestions. Since I have enough time, will start with the tutorial you have mentioned.
– ovezn
Mar 11 at 1:02
add a comment |
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What do you need to segment? I'd be nice so see some labels of the segmentation.
You may not need to enhance the image, if all your dataset has that same amount of noise, the network will generalize properly.
Regarding CNNs architectures, it depends on the constraints you have with processing power and accuracy. If that is not a constrain go with something like MaskRCNN, check that repo as a good starting point, some results are like this:
Be mindful it's a bit of a complex architecture so inference times might be a bit too high (but it's doable on realtime depending your gpu).
Other simple architectures are FCN (Fully Convolutional Networks) with are basically your CNN but instead of fully connected layers:
You replace with with Fully Convolutional Layers:
Images taken from HERE.
The advantage of this FCNs are that they are really easy to implement and modify since you can go with simple architectures (FCN-Alexnet), to more complex and more accurate ones (FCN-VGG, FCN-Resnet).
Also, I think you don't mention framework, there are many to choose from and it depends on your familiarly with languages, most of them you can do them with python:
- TensorFlow
- Pytorch
- MXNet
But if you are a beginner, try starting with a GUI based one, Nvidia Digits is a great starting point and really easy to configure, it's based on Caffe so it's fairly fast when deploying and can easily be integrated with accelerators like TensorRT.
answered Mar 8 at 9:22
bpinayabpinaya
176110
Thank you @bpinaya for your detailed reply. My project is to do automatic detection of faults in hull (main body of the ship) using CNN. I want to segment the faulty areas such as broken structures and cracks in the image. We can assume most of the pictures will be having same amount of noises.
– ovezn
Mar 9 at 11:52
I am a beginner to neural networks. The basic knowledge I have is how a neural network and cnn works. I have some hands on in python. As of now i am able to build a neural network for classification and regression by editing already written code according to my needs. So I am not a programming person and from core engineering field who was put incharge of doing a automation work. I can get a basic idea from your reply but not able to understand the full meaning because i am in a beginner level. But I will take this as a thread and try to learn from the hints.
– ovezn
Mar 9 at 11:52
1
In that case you need to start from scratch, I'll suggest you take a course regarding CNNs first, since that'll help you understand what you are coding, the best one out there might be this coursera.org/specializations/deep-learning , but then again it's paid, but it's worth it. If you have a hard deadline try Nvidia Digits (the link I posted), install it and fine tune already trained networks.
– bpinaya
Mar 10 at 17:16
Thank you so much for your valuable suggestions. Since I have enough time, will start with the tutorial you have mentioned.
– ovezn
Mar 11 at 1:02
add a comment |
What do you need to segment? I'd be nice so see some labels of the segmentation.
You may not need to enhance the image, if all your dataset has that same amount of noise, the network will generalize properly.
Regarding CNNs architectures, it depends on the constraints you have with processing power and accuracy. If that is not a constrain go with something like MaskRCNN, check that repo as a good starting point, some results are like this:
Be mindful it's a bit of a complex architecture so inference times might be a bit too high (but it's doable on realtime depending your gpu).
Other simple architectures are FCN (Fully Convolutional Networks) with are basically your CNN but instead of fully connected layers:
You replace with with Fully Convolutional Layers:
Images taken from HERE.
The advantage of this FCNs are that they are really easy to implement and modify since you can go with simple architectures (FCN-Alexnet), to more complex and more accurate ones (FCN-VGG, FCN-Resnet).
Also, I think you don't mention framework, there are many to choose from and it depends on your familiarly with languages, most of them you can do them with python:
- TensorFlow
- Pytorch
- MXNet
But if you are a beginner, try starting with a GUI based one, Nvidia Digits is a great starting point and really easy to configure, it's based on Caffe so it's fairly fast when deploying and can easily be integrated with accelerators like TensorRT.
answered Mar 8 at 9:22
bpinayabpinaya
176110
Thank you @bpinaya for your detailed reply. My project is to do automatic detection of faults in hull (main body of the ship) using CNN. I want to segment the faulty areas such as broken structures and cracks in the image. We can assume most of the pictures will be having same amount of noises.
– ovezn
Mar 9 at 11:52
I am a beginner to neural networks. The basic knowledge I have is how a neural network and cnn works. I have some hands on in python. As of now i am able to build a neural network for classification and regression by editing already written code according to my needs. So I am not a programming person and from core engineering field who was put incharge of doing a automation work. I can get a basic idea from your reply but not able to understand the full meaning because i am in a beginner level. But I will take this as a thread and try to learn from the hints.
– ovezn
Mar 9 at 11:52
1
In that case you need to start from scratch, I'll suggest you take a course regarding CNNs first, since that'll help you understand what you are coding, the best one out there might be this coursera.org/specializations/deep-learning , but then again it's paid, but it's worth it. If you have a hard deadline try Nvidia Digits (the link I posted), install it and fine tune already trained networks.
– bpinaya
Mar 10 at 17:16
Thank you so much for your valuable suggestions. Since I have enough time, will start with the tutorial you have mentioned.
– ovezn
Mar 11 at 1:02
add a comment |
What do you need to segment? I'd be nice so see some labels of the segmentation.
You may not need to enhance the image, if all your dataset has that same amount of noise, the network will generalize properly.
Regarding CNNs architectures, it depends on the constraints you have with processing power and accuracy. If that is not a constrain go with something like MaskRCNN, check that repo as a good starting point, some results are like this:
Be mindful it's a bit of a complex architecture so inference times might be a bit too high (but it's doable on realtime depending your gpu).
Other simple architectures are FCN (Fully Convolutional Networks) with are basically your CNN but instead of fully connected layers:
You replace with with Fully Convolutional Layers:
Images taken from HERE.
The advantage of this FCNs are that they are really easy to implement and modify since you can go with simple architectures (FCN-Alexnet), to more complex and more accurate ones (FCN-VGG, FCN-Resnet).
Also, I think you don't mention framework, there are many to choose from and it depends on your familiarly with languages, most of them you can do them with python:
- TensorFlow
- Pytorch
- MXNet
But if you are a beginner, try starting with a GUI based one, Nvidia Digits is a great starting point and really easy to configure, it's based on Caffe so it's fairly fast when deploying and can easily be integrated with accelerators like TensorRT.
answered Mar 8 at 9:22
bpinayabpinaya
176110
What do you need to segment? I'd be nice so see some labels of the segmentation.
You may not need to enhance the image, if all your dataset has that same amount of noise, the network will generalize properly.
Regarding CNNs architectures, it depends on the constraints you have with processing power and accuracy. If that is not a constrain go with something like MaskRCNN, check that repo as a good starting point, some results are like this:
Be mindful it's a bit of a complex architecture so inference times might be a bit too high (but it's doable on realtime depending your gpu).
Other simple architectures are FCN (Fully Convolutional Networks) with are basically your CNN but instead of fully connected layers:
You replace with with Fully Convolutional Layers:
Images taken from HERE.
The advantage of this FCNs are that they are really easy to implement and modify since you can go with simple architectures (FCN-Alexnet), to more complex and more accurate ones (FCN-VGG, FCN-Resnet).
Also, I think you don't mention framework, there are many to choose from and it depends on your familiarly with languages, most of them you can do them with python:
- TensorFlow
- Pytorch
- MXNet
But if you are a beginner, try starting with a GUI based one, Nvidia Digits is a great starting point and really easy to configure, it's based on Caffe so it's fairly fast when deploying and can easily be integrated with accelerators like TensorRT.
answered Mar 8 at 9:22
bpinayabpinaya
176110
answered Mar 8 at 9:22
bpinayabpinaya
176110
answered Mar 8 at 9:22
bpinayabpinaya
176110
answered Mar 8 at 9:22
bpinayabpinaya
176110
176110
Thank you @bpinaya for your detailed reply. My project is to do automatic detection of faults in hull (main body of the ship) using CNN. I want to segment the faulty areas such as broken structures and cracks in the image. We can assume most of the pictures will be having same amount of noises.
– ovezn
Mar 9 at 11:52
I am a beginner to neural networks. The basic knowledge I have is how a neural network and cnn works. I have some hands on in python. As of now i am able to build a neural network for classification and regression by editing already written code according to my needs. So I am not a programming person and from core engineering field who was put incharge of doing a automation work. I can get a basic idea from your reply but not able to understand the full meaning because i am in a beginner level. But I will take this as a thread and try to learn from the hints.
– ovezn
Mar 9 at 11:52
1
In that case you need to start from scratch, I'll suggest you take a course regarding CNNs first, since that'll help you understand what you are coding, the best one out there might be this coursera.org/specializations/deep-learning , but then again it's paid, but it's worth it. If you have a hard deadline try Nvidia Digits (the link I posted), install it and fine tune already trained networks.
– bpinaya
Mar 10 at 17:16
Thank you so much for your valuable suggestions. Since I have enough time, will start with the tutorial you have mentioned.
– ovezn
Mar 11 at 1:02
add a comment |
Thank you @bpinaya for your detailed reply. My project is to do automatic detection of faults in hull (main body of the ship) using CNN. I want to segment the faulty areas such as broken structures and cracks in the image. We can assume most of the pictures will be having same amount of noises.
– ovezn
Mar 9 at 11:52
I am a beginner to neural networks. The basic knowledge I have is how a neural network and cnn works. I have some hands on in python. As of now i am able to build a neural network for classification and regression by editing already written code according to my needs. So I am not a programming person and from core engineering field who was put incharge of doing a automation work. I can get a basic idea from your reply but not able to understand the full meaning because i am in a beginner level. But I will take this as a thread and try to learn from the hints.
– ovezn
Mar 9 at 11:52
1
In that case you need to start from scratch, I'll suggest you take a course regarding CNNs first, since that'll help you understand what you are coding, the best one out there might be this coursera.org/specializations/deep-learning , but then again it's paid, but it's worth it. If you have a hard deadline try Nvidia Digits (the link I posted), install it and fine tune already trained networks.
– bpinaya
Mar 10 at 17:16
Thank you so much for your valuable suggestions. Since I have enough time, will start with the tutorial you have mentioned.
– ovezn
Mar 11 at 1:02
Thank you @bpinaya for your detailed reply. My project is to do automatic detection of faults in hull (main body of the ship) using CNN. I want to segment the faulty areas such as broken structures and cracks in the image. We can assume most of the pictures will be having same amount of noises.
– ovezn
Mar 9 at 11:52
Thank you @bpinaya for your detailed reply. My project is to do automatic detection of faults in hull (main body of the ship) using CNN. I want to segment the faulty areas such as broken structures and cracks in the image. We can assume most of the pictures will be having same amount of noises.
– ovezn
Mar 9 at 11:52
I am a beginner to neural networks. The basic knowledge I have is how a neural network and cnn works. I have some hands on in python. As of now i am able to build a neural network for classification and regression by editing already written code according to my needs. So I am not a programming person and from core engineering field who was put incharge of doing a automation work. I can get a basic idea from your reply but not able to understand the full meaning because i am in a beginner level. But I will take this as a thread and try to learn from the hints.
– ovezn
Mar 9 at 11:52
I am a beginner to neural networks. The basic knowledge I have is how a neural network and cnn works. I have some hands on in python. As of now i am able to build a neural network for classification and regression by editing already written code according to my needs. So I am not a programming person and from core engineering field who was put incharge of doing a automation work. I can get a basic idea from your reply but not able to understand the full meaning because i am in a beginner level. But I will take this as a thread and try to learn from the hints.
– ovezn
Mar 9 at 11:52
1
1
In that case you need to start from scratch, I'll suggest you take a course regarding CNNs first, since that'll help you understand what you are coding, the best one out there might be this coursera.org/specializations/deep-learning , but then again it's paid, but it's worth it. If you have a hard deadline try Nvidia Digits (the link I posted), install it and fine tune already trained networks.
– bpinaya
Mar 10 at 17:16
In that case you need to start from scratch, I'll suggest you take a course regarding CNNs first, since that'll help you understand what you are coding, the best one out there might be this coursera.org/specializations/deep-learning , but then again it's paid, but it's worth it. If you have a hard deadline try Nvidia Digits (the link I posted), install it and fine tune already trained networks.
– bpinaya
Mar 10 at 17:16
Thank you so much for your valuable suggestions. Since I have enough time, will start with the tutorial you have mentioned.
– ovezn
Mar 11 at 1:02
Thank you so much for your valuable suggestions. Since I have enough time, will start with the tutorial you have mentioned.
– ovezn
Mar 11 at 1:02
add a comment |
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