Anomaly detection in Side scan video

[bishtdeepakhydro]

I have been trying SSD, CNN and yolo, but the anomally (high amplitude detection) in a side scan video or images. But all not detecting the same. Kindly suggest way ahead for this detection in side scan waterfall video file.

the attached image shows an anomally in brighter pixels.

[glenn-jocher]

@bishtdeepakhydro side scan sonar images or videos. Traditional object detection models like SSD, CNN, and YOLO might not be the best choice for this task, as they are generally used for detecting and classifying objects in natural images.

For anomaly detection in sonar images, you might want to explore some of the following techniques:

Autoencoders: Autoencoders are a type of unsupervised learning technique that can learn to encode and reconstruct input data. You can train an autoencoder on normal side scan sonar images, and then use the reconstruction error to detect anomalies. High reconstruction errors could indicate the presence of an anomaly.

Image Segmentation: Since you are looking for high amplitude regions in the image, you can treat this as an image segmentation problem. You can try using U-Net, which is a popular convolutional neural network for image segmentation. Train the U-Net on labeled side scan sonar images where the high amplitude regions are annotated, and then use the trained model to predict the high amplitude regions in new images.

Adaptive Thresholding: You can use adaptive thresholding to identify high amplitude regions in the sonar images. This technique calculates a threshold for each pixel based on the surrounding pixels' intensities, and then classifies the pixel as high or low amplitude. OpenCV library provides functions to perform adaptive thresholding. Experiment with different window sizes and parameters to fine-tune the results.

[bishtdeepakhydro]

Thanks a ton for the apt response. Can you suggest a suitable link to Autoencoders for the this problem of wreck detection/ anomaly resembling geometric shapes in side scan water fall display.

[glenn-jocher]

@bishtdeepakhydro for your specific application of wreck detection and anomaly detection in side scan sonar images, I would recommend diving into the Ultralytics documentation on tasks that are closely related to anomaly detection. While we don't have a dedicated section on autoencoders, you can explore the segmentation task documentation, which might provide insights into how to approach the problem of detecting geometric shapes and anomalies.

The segmentation task documentation can guide you on how to train models that can segment specific regions of interest in images, which is somewhat analogous to identifying anomalies. Although it's not directly focused on autoencoders, the principles of segmentation could be adapted for your needs.

Please visit the Ultralytics Docs and navigate to the section on segmentation for more information. Remember, while Ultralytics doesn't directly provide autoencoder models, the concepts and techniques from segmentation tasks can be a valuable starting point for your anomaly detection project. Good luck with your side scan sonar image analysis! 🚀🌊

[bishtdeepakhydro]

Thank you for your valuable insights and information.I will be shortly posting the developed code (to detect wrecks in Side scan sonar images along with real time detection mode) on GitHub for further development and outreach.Warm RegardsDeepak On 06-Feb-2024 3:09 pm, Glenn Jocher ***@***.***> wrote: @bishtdeepakhydro for your specific application of wreck detection and anomaly detection in side scan sonar images, I would recommend diving into the Ultralytics documentation on tasks that are closely related to anomaly detection. While we don't have a dedicated section on autoencoders, you can explore the segmentation task documentation, which might provide insights into how to approach the problem of detecting geometric shapes and anomalies. The segmentation task documentation can guide you on how to train models that can segment specific regions of interest in images, which is somewhat analogous to identifying anomalies. Although it's not directly focused on autoencoders, the principles of segmentation could be adapted for your needs. Please visit the Ultralytics Docs and navigate to the section on segmentation for more information. Remember, while Ultralytics doesn't directly provide autoencoder models, the concepts and techniques from segmentation tasks can be a valuable starting point for your anomaly detection project. Good luck with your side scan sonar image analysis! 🚀🌊 —Reply to this email directly, view it on GitHub, or unsubscribe.You are receiving this because you were mentioned.Message ID: ***@***.***>

[pderrenger]

@bishtdeepakhydro hello Deepak,

It's great to hear that you're working on a project for wreck detection in side scan sonar images and planning to share your code on GitHub for community collaboration. Anomaly detection in such specialized domains can indeed be challenging, and it seems like you're exploring various deep learning approaches to tackle it.

For the type of anomaly you're trying to detect, characterized by high amplitude or brighter pixels, you might want to consider a few strategies:

1. Data Preprocessing: Enhance the contrast of the anomalies in the preprocessing stage to make them more distinguishable for the model.

2. Custom Dataset: If you haven't already, create a labeled dataset where the anomalies are clearly annotated. This will be crucial for supervised learning models like YOLO to learn effectively.

3. Fine-tuning: Use a pretrained model like YOLOv8 and fine-tune it on your custom dataset. YOLOv8 is designed to be adaptable to various object detection tasks and could potentially be effective for your use case.

4. Post-processing: Implement post-processing steps to filter out detections based on specific criteria, such as brightness or size, to improve the accuracy of anomaly detection.

5. Model Ensembling: Combine the outputs of different models (SSD, CNN, YOLO) to improve detection performance. Each model may capture different aspects of the data, and ensembling can leverage their strengths.

6. Transfer Learning: Utilize transfer learning to adapt a model trained on a similar task to your specific anomaly detection problem.

Remember to share clear documentation and usage instructions along with your code to help others understand and contribute to your project. If you have any specific questions or need further assistance with YOLOv8, feel free to reach out.

Looking forward to seeing your work on GitHub, and best of luck with your project! 🚀🌊

Warm