用于水下前视声纳图像检测的阴影捕获深度神经网络

Taowen Xiao, Zijian Cai, Cong Lin, Qiong Chen
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引用次数: 0

摘要

图像声纳是一种广泛应用于水下目标探测的无线通信技术,但在探测过程中,由于设备分辨率的不足,往往导致目标识别难度加大。鉴于人工智能技术在水下无线通信研究领域取得的显著成果,我们提出了一种基于卷积神经网络(CNN)和阴影信息捕获的目标检测方法,通过充分利用目标的阴影信息来提高水下声纳图像的目标识别和定位效果。我们设计了一个阴影捕获模块(SCM)来捕获特征映射中的阴影信息并加以利用。SCM兼容参数增量较小且具有一定可移植性的CNN模型,通过参考阴影特征,可以有效缓解因设备分辨率不足而带来的识别困难。通过在鹏程实验室提供的水下声纳数据集上的大量实验,所提出的方法可以有效地改善CNN模型的特征表示,增强类与类特征的差异性。在PASCAL VOC 2012的主要评价标准下,本文提出的方法在IOU阈值为0.7的情况下,将平均精度(mAP)从69.61%提高到75.73%,超过了许多现有的传统深度学习模型,而我们提出的模块的轻量化设计更有利于人工智能技术在水下无线通信领域的实现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Shadow Capture Deep Neural Network for Underwater Forward-Looking Sonar Image Detection
Image sonar is a widely used wireless communication technology for detecting underwater objects, but the detection process often leads to increased difficulty in object identification due to the lack of equipment resolution. In view of the remarkable results achieved by artificial intelligence techniques in the field of underwater wireless communication research, we propose an object detection method based on convolutional neural network (CNN) and shadow information capture to improve the object recognition and localization effect of underwater sonar images by making full use of the shadow information of the object. We design a Shadow Capture Module (SCM) that can capture the shadow information in the feature map and utilize them. SCM is compatible with CNN models that have a small increase in parameters and a certain degree of portability, and it can effectively alleviate the recognition difficulties caused by the lack of device resolution through referencing shadow features. Through extensive experiments on the underwater sonar data set provided by Pengcheng Lab, the proposed method can effectively improve the feature representation of the CNN model and enhance the difference between class and class features. Under the main evaluation standard of PASCAL VOC 2012, the proposed method improved from an average accuracy (mAP) of 69.61% to 75.73% at an IOU threshold of 0.7, which exceeds many existing conventional deep learning models, while the lightweight design of our proposed module is more helpful for the implementation of artificial intelligence technology in the field of underwater wireless communication.
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