Underwater sonar objective detection plays an important role in the field of ocean exploration. In order to solve the problem of sonar objective detection under the complex environment, a sonar objective detection method is proposed based on dilated separable densely connected convolutional neural networks (DS-CNNs) and quantum-behaved particle swarm optimization (QPSO) algorithm. Firstly, the dilated separable convolution kernel is proposed to extend the local receptive field and enhance the feature extraction ability of the convolution layers. Secondly, based on the linear interpolation algorithm, a multisampling pooling (MS-pooling) operation is proposed to reduce the feature information loss and restore image resolution. At last, with contraction-expansion factor and difference variance in the traditional particle swarm optimization algorithm introduced, the QPSO algorithm is employed to optimize the weight parameters of the network model. The proposed method is validated on the sonar image dataset and is compared with other existing methods. Using DS-CNNs to detect different kinds of sonar objectives, the experiments shows that the detection accuracy of DS-CNNs reaches 96.98% and DS-CNNs have better detection effect and stronger robustness.

中文翻译：

---

基于膨胀可分离密集连接CNN和量子行为PSO算法的声纳目标检测

水下声纳客观检测在海洋勘探领域中起着重要作用。为了解决复杂环境下声纳目标检测的问题，提出了一种基于扩张可分离的紧密连接卷积神经网络（DS-CNN）和量子行为粒子群优化（QPSO）算法的声纳目标检测方法。首先，提出了扩张的可分离卷积核，以扩展局部接收场并增强卷积层的特征提取能力。其次，基于线性插值算法，提出了一种多采样池（MS-pooling）操作，以减少特征信息的丢失并恢复图像分辨率。最后，在引入传统粒子群优化算法的基础上，结合伸缩因子和差异方差，采用QPSO算法对网络模型的权重参数进行优化。该方法在声纳图像数据集上得到验证，并与其他现有方法进行了比较。通过DS-CNNs检测不同种类的声纳目标，实验表明DS-CNNs的检测精度达到96.98％，并且DS-CNNs具有更好的检测效果和较强的鲁棒性。