特征图强化网络:利用特征图强化船舶检测模型训练的网络结构  

FMRNet: A network structure for enhanced ship detection model trainingusing feature maps

作　　者：张泽琨 谭震彪 余坤 方斌[1] 黄骁 马杰[1] ZHANG Zekun;TAN Zhenbiao;YU Kun;FANG Bing;HUANG Xiao;MA Jie(National Key Laboratory of Science and Technology on Multispectral Information Processing,School of Artificial Intelligence and Automation,Huazhong University of Science and Technology,Wuhan 430072,China;China Ship Development,and Design Center,Wuhan 430064,China;China Railway Siyuan Survey and Design Group,Wuhan 430063,China)

机构地区：[1]华中科技大学人工智能及自动化学院多谱信息技术处理国家重点实验室,武汉430072 [2]中国舰船研究设计中心,武汉430064 [3]中铁第四勘察设计院集团有限公司,武汉430063

出　　处：《遥感学报》2023年第12期2697-2705,共9页NATIONAL REMOTE SENSING BULLETIN

基　　金：国家自然科学基金(编号:U1913602)。

摘　　要：随着人工智能技术的进一步发展,深度学习方法在船舶检测领域发挥着重要作用。然而,深度学习算法出现的虚警和漏检,对船舶检测领域技术的应用存在一定的阻碍。虽然经典的深度学习方法能够有效处理单一背景的海面,但是当面对复杂背景之下的数据时,经典模型很容易得出岸上的虚警。并且在常规训练中,模型常常对一些显著特征过于关注,出现特征过拟合现象,当这些显著特征发生改变时极易出现漏检。在模型对输入进行前向传播的过程中,模型中不同网络层会对输入生成对应的映射,也就是特征图。充分利用特征图的语义信息和空间信息是一种有效减少虚警和漏检的方法。与传统模型相比,我们提出的特征图强化网络可以充分利用特征图生成自适应特征图掩码与水陆分割掩码,避免模型的特征过拟合与削弱复杂背景造成的影响,最终达到减少虚警与漏检的目的。在现有公共数据集上与算法模型的对比实验结果表明,本文所提出的方法的性能更为出色,超过了其他SOTA算法。With the ongoing development of artificial intelligence technology,deep learning methods have become increasingly important role in the field of ship detection.However,the false alarms and missed detections that appear in deep learning algorithms hinder the application of technology in the field of ship detection.Although the classical deep learning methods can effectively deal with a singlebackground sea surface,the classical models can easily yield false alarms on shore when faced with data under complex backgrounds.In custom training,the model often tends to overly emphasize some salient features,which leads to feature overfitting.Detection can easily be missed when these salient features change.In the process of forward propagation of the model to the input,different network layers in the model generate corresponding mappings or feature maps from the input.Fully utilizing the semantic and spatial information of the feature maps is an effective way to reduce false alarms and missed detections.Compared with the traditional model,our proposed Feature Map Reinforcement Network(FMRNnet)can fully utilize feature maps to generate adaptive feature map masks and water–land segmentation masks.This method ultimately reduces false alarms and missed detections by avoiding feature overfitting of the model and weakening the effects caused by complex backgrounds.In FMRNnet,we design the Self Feature-map Mask Module(SFMM),which can selectively utilize the feature map through the attention mechanism for generating an adaptive mask.The mask prevents the model from focusing on a single feature point,which prevents feature overfitting.We also propose a Feature-map Sea-Land Segmentation Module(FSSM)that is parallel to SFMM.It reduces the false alarms of ship targets appearing in the land area by introducing the fusion between the water-land segmentation mask and the feature map.The experimental results,when compared with SOTA algorithms on publicly available datasets,show that the performance of the proposed method in this study

关 键 词：遥感成像 人工智能 船舶目标检测 神经网络 特征图 网络强化 旋转目标检测 抑制过拟合 

分 类 号：P2[天文地球—测绘科学与技术]