Sonar Target Detection Based on a Dual Channel Attention Convolutional Network

Due to the complexity and diversity of underwater environment, high-precision and fast target detection is a scientific problem in underwater acoustic information extraction, especially the underwater target detection of sonar images still has a technical bottleneck. With the development of intelligent detection technology, as the state of the art model, target detection model based on deep neural network adopts different scale feature extraction mechanism, which is easy to generate false alarm for important targets and difficult to overcome the contradiction between false detection and missed detection. The attention mechanism can fully learn the features of the target and improve the accuracy of target detection. Considering the characteristics of seabed exploration task and underwater target, we propose a deep convolution network based on dual channel attention mechanism (DCNet), This model can strengthen the features of the target of interest while weakening the irrelevant background noise information, so as to improve the detection accuracy of the target and enhance the detection ability of the underwater target. The experimental results show that the average accuracy of the dual channel attention mechanism can achieve higher accuracy than the original model, and is superior to other target detection models in accuracy and performance. This research has important practical significance for improving the task of underwater target detection of sonar images and has a wide range of engineering application prospects in the detection of underwater acoustic systems.