Deep Learning from Shallow Dives: Sonar Image Generation and Training for Underwater Object Detection (1810.07990v1)

Abstract: Among underwater perceptual sensors, imaging sonar has been highlighted for its perceptual robustness underwater. The major challenge of imaging sonar, however, arises from the difficulty in defining visual features despite limited resolution and high noise levels. Recent developments in deep learning provide a powerful solution for computer-vision researches using optical images. Unfortunately, deep learning-based approaches are not well established for imaging sonars, mainly due to the scant data in the training phase. Unlike the abundant publically available terrestrial images, obtaining underwater images is often costly, and securing enough underwater images for training is not straightforward. To tackle this issue, this paper presents a solution to this field's lack of data by introducing a novel end-to-end image-synthesizing method in the training image preparation phase. The proposed method present image synthesizing scheme to the images captured by an underwater simulator. Our synthetic images are based on the sonar imaging models and noisy characteristics to represent the real data obtained from the sea. We validate the proposed scheme by training using a simulator and by testing the simulated images with real underwater sonar images obtained from a water tank and the sea.

• The paper proposes generating synthetic sonar images using a simulator and neural style transfer (StyleBankNet) to overcome the scarcity of real underwater training data for deep learning.
• Validation shows that deep learning models trained on these synthetic sonar images achieve comparable object detection performance (AP up to 0.77 in tanks, 0.63 in sea) to models trained on real data.
• This method offers a resource-efficient solution for training underwater object detection systems, potentially reducing costly data collection and advancing autonomous maritime technologies.

Deep Learning from Shallow Dives: Sonar Image Generation and Training for Underwater Object Detection

The paper "Deep Learning from Shallow Dives: Sonar Image Generation and Training for Underwater Object Detection" by Sejin Lee, Byungjae Park, and Ayoung Kim addresses the challenging task of underwater object detection using imaging sonar. While imaging sonar is essential in scenarios where optical visibility is compromised, it still suffers from inherent limitations such as low resolution and high noise levels. These characteristics impede the direct application of deep learning techniques, which have significantly advanced in computer vision through optical imagery. The authors propose a solution to the limited availability of training data for sonar imaging, which is a pivotal issue in applying deep learning techniques effectively.

Overview of Contributions

The authors introduce a novel data generation pipeline using a simulator, enabling the creation of synthetic sonar images intended for training purposes.

1. Synthetic Image Generation: The proposal focuses on an end-to-end image-synthesizing method leveraging a simulator to generate sonar images. The images are stylized using StyleBankNet, a neural network architecture capable of transferring image styles representative of real underwater noise and texture.
2. Validation: The synthetic images are tested against real sonar images acquired from controlled environments like water tanks and the sea. Results demonstrate comparable performance to networks trained on real-world data, potentially eliminating the need for extensive real-world data collection.
3. Real-world Applicability: Validation with images from varied sonar sensors highlights the broad applicability of the proposed method across different underwater conditions and environments.

Numerical Results and Claims

The paper provides a comprehensive series of evaluations indicating that the network trained with synthetic sonar images performs on par with networks trained with real sonar images collected from the sea. Specifically, the precision-recall curves present promising average precision metrics: testing with synthetic data achieves an average precision of 0.77 in water tanks and 0.63 in sea environments.

The evaluation of StyleBankNet style transfer indicates that the network successfully learns the noise characteristics of real sonar images, with epoch evolution yielding clearer and more accurate representations of objects within a simulated scene. This validation underscores the potential for synthetic data generation to mitigate the data scarcity issue that typically accompanies underwater sensor deployments.

Implications and Future Directions

The work provides substantial implications for the deployment of sonar-based object detection systems. It suggests a pathway for reducing the dependency on costly and labor-intensive data collection processes typically necessary for training deep learning models. Furthermore, this approach may foster advancements in autonomous underwater vehicles and other maritime technologies, potentially enhancing operational ranges and detection capabilities.

From a theoretical perspective, StyleBankNet’s effective style transfer raises interesting questions around the development of adaptive learning mechanisms that could automatically tailor trained models to specific environmental conditions. Future developments could explore more sophisticated domain adaptation techniques or integrate this image synthesizing methodology into broader unsupervised learning frameworks for enhanced underwater object classification.

In conclusion, this paper contributes to the understanding and practical application of deep learning in the underwater domain, offering a creative and resource-efficient solution to challenges posed by sonar image data scarcity.