结合区域提取和改进卷积神经网络的水下小目标检测

针对水下小目标信息量有限而难以提取有效特征导致的检测性能不佳问题，提出了一种结合区域提取和融合Hu矩特征的改进卷积神经网络水下小目标检测方法。该方法包含区域提取和分类两个步骤。首先以马尔可夫随机场分割算法为基础进行区域提取，对潜在目标定位的同时降低伪目标对后续分类的干扰；然后提取潜在目标区域的Hu矩特征并融入卷积神经网络，形成一种形状特征表征能力更强的改进卷积神经网络用于分类。声呐实测数据处理结果表明，该方法可以有效提升对水下小目标的发现概率和正确报警率，与其他目标检测方法相比，该方法具有更好的检测性能和泛化性。

Integrating region extraction with improved convolutional neural network for underwater small object detection

Due to the limited feature information of underwater small objects, it is difficult to extract effective features, resulting in poor detection performance. Aiming at this problem, an underwater small object detection method combining region extraction and improved convolutional neural network fused with Hu moment features is proposed. The method includes two stages of region extraction and classification. Firstly, a region extraction method based on the Markov random field segmentation algorithm is used to locate potential objects and reduce the interference of false objects on subsequent classification. Then, Hu moment features of potential object regions are extracted and fused with the convolutional neural network to form an improved network with stronger characterization ability of shape features for classification. The results of sonar data processing show that the method can effectively elevate the detection probability and correct alarm rate of underwater small objects. Compared with the common object detection methods, the proposed method has superior detection performance and generalization.