Sonar Image Detection Based on Multi-Scale Multi-Column Convolution Neural Networks

Abstract

Automatic detection of underwater objects by sonar images is an important and challenging topic in applications of Autonomous Underwater Vehicle (AUV) under the complex marine environment. A detection method is proposed based on Multi-Scale Multi-Column Convolution Neural Networks (MSMC-CNNs). Firstly, the Multi-Scale Multi-Column CNNs is used to form an encoder network for extracting multi-scale features of the sonar image. Secondly, the bicubic linear interpolation algorithm is used as the deconvolution process of the decoder networks to restore the sonar image size and resolution. Moreover, a novel transfer learning manner based on progressive fine-tuning to accelerate the model training. Finally, the proposed method is validated on the sonar image dataset and is compared with other existing detection methods. The pixel accuracy (PA) of MSMC-CNNs for different categories sonar image is over 95%. The experiment results show that the MSMC-CNNs model has better detection effect and more robustness to noise.

Keywords

• Underwater object detection
• MSMC-CNNs
• bicubic linear interpolation algorithm
• deconvolution