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

张泽琨; 谭震彪; 余坤; 方斌; 黄骁; 马杰

doi:10.11834/jrs.20221656

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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 training using feature maps
2023年27卷第12期页码：2697-2705
收稿：2021-10-20，

纸质出版：2023-12-07
DOI： 10.11834/jrs.20221656
稿件说明：

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张泽琨，谭震彪，余坤，方斌，黄骁，马杰.2023.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.遥感学报，27（12）： 2697-2705 DOI： 10.11834/jrs.20221656.

Zhang Z K，Tan Z B，Yu K，Fang B，Huang X and Ma J. 2023. FMRNet： A network structure for enhanced ship detection model training using feature maps. National Remote Sensing Bulletin， 27（12）：2697-2705 DOI： 10.11834/jrs.20221656.

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。

Abstract

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 single-background 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 is excellent and outperforms that of other SOTA algorithms. After FMRNet is added

the 10-fold average mAP value of the detection algorithm ROI trans largely improves. This enhancement increases the mean value of baseline mAP from 86.1% to 90.8%

which surpasses that of other SOTA algorithms. Benefiting from the adaptive mask

the mAP value of the model including the SFMM module is 90.4%

which achieves a 4.2% improvement over the baseline. Owing to the priori knowledge learned from the water-land distribution

FSSM improves the precision and recall of the model

which results in a MAP value of 86.4%. For the task of ship detection

we propose a novel backbone network

that is

FMRNet based on Resnet. Our proposed SFMM module enables the model to discern the target from multiple features for avoiding the overfitting of salient features. We design the FSSM module to reduce the false alarms caused by complex backgrounds. Suppressing the non-water surface area reduces the confidence level of targets appearing in non-water surface. FSSM achieves the purpose of removing unreasonable false alarms while improving the accuracy of the model.

关键词

Keywords

references

Bi F K , Hou J Y , Chen L , Yang Z H and Wang Y P . 2019 . Ship detection for optical remote sensing images based on visual attention enhanced network . Sensors , 19 ( 10 ): 2271 [ DOI: 10.3390/s19102271 http://dx.doi.org/10.3390/s19102271 ]

Ding G D , Kha S , Tang Z M and Porikli F . 2017 . Let features decide for themselves: feature mask network for person re-identification. arXiv: 1711 . 07155

Ding J , Xue N , Long Y , Xia G S and Lu Q K . 2018 . Learning RoI transformer for detecting oriented objects in aerial images. arXiv: 1812 . 00155

Girshick R . 2015 . Fast R-CNN // 2015 IEEE International Conference on Computer Vision . Santiago : IEEE: 1440 - 1448 [ DOI: 10.1109/ICCV.2015.169 http://dx.doi.org/10.1109/ICCV.2015.169 ]

Goetz A F H , Rock B N and Rowan L C . 1983 . Remote sensing for exploration; an overview . Economic Geology , 78 ( 4 ): 573 - 590 [ DOI: 10.2113/gsecongeo.78.4.573 http://dx.doi.org/10.2113/gsecongeo.78.4.573 ]

Gu Z Y . 2010 . Research on high performance computing platform of ocean environmental information processing // 2010 International Conference on Optics, Photonics and Energy Engineering . Wuhan : IEEE: 298 - 300 [ DOI: 10.1109/OPEE.2010.5508128 http://dx.doi.org/10.1109/OPEE.2010.5508128 ]

He K M , Zhang X Y , Ren S Q and Sun J . 2016 . Deep residual learning for image recognition // 2016 IEEE Conference on Computer Vision and Pattern Recognition . Las Vegas : IEEE: 770 - 778 [ DOI: 10.1109/CVPR.2016.90 http://dx.doi.org/10.1109/CVPR.2016.90 ]

Hu J , Shen L and Sun G . 2018 . Squeeze-and-excitation networks // 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Salt Lake City : IEEE: 7132 - 7141 [ DOI: 10.1109/CVPR.2018.00745 http://dx.doi.org/10.1109/CVPR.2018.00745 ]

Lunetta R S , Congalton R C , Fenstermaker L K , Jensen J R , McGwire K C and Tinney L R . 1991 . Remote sensing and geographic information system data integration: error sources and research issues . Photogrammetric Engineering and Remote Sensing , 57 ( 6 ): 677 - 687

Nie X , Duan M Y , Ding H X , Hu B L and Wong E K . 2020 . Attention mask R-CNN for ship detection and segmentation from remote sensing images . IEEE Access , 8 : 9325 - 9334 [ DOI: 10.1109/ACCESS.2020.2964540 http://dx.doi.org/10.1109/ACCESS.2020.2964540 ]

Qian W , Yang X , Peng S L , Guo Y and Yan J C . 2019 . Learning modulated loss for rotated object detection. arXiv: 1911 . 08299

Redmon J , Divvala S , Girshick R and Farhadi A . 2016 . You only look once : unified, real-time object detection// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition . Las Vegas : IEEE: 779 - 788 [ DOI: 10.1109/CVPR.2016.91 http://dx.doi.org/10.1109/CVPR.2016.91 ]

Redmon J and Farhadi A . 2018 . YOLOv3: an incremental improvement. arXiv: 1804 . 02767 v 1

Simonyan K and Zisserman A . 2015 . Very deep convolutional networks for large-scale image recognition. arXiv:1409 . 1556

Xu K Y , Hu W H and Chen R . 2006 . A vessel targets detection method based on Canny algorithm . Infrared Technology , 28 ( 6 ): 315 - 318

许开宇 , 胡文骅 , 陈仁 . 2006 . 一种基于Canny算子的船舶目标检测算法 . 红外技术 , 28 ( 6 ): 315 - 318 [ DOI: 10.3969/j.issn.1001-8891.2006.06.002 http://dx.doi.org/10.3969/j.issn.1001-8891.2006.06.002 ]

Xu Y C , Fu M T , Wang Q M , Wang Y K , Chen K , Xia G S and Bai X . 2021 . Gliding vertex on the horizontal bounding box for multi-oriented object detection . IEEE Transactions on Pattern Analysis and Machine Intelligence , 43 ( 4 ): 1452 - 1459 [ DOI: 10.1109/TPAMI.2020.2974745 http://dx.doi.org/10.1109/TPAMI.2020.2974745 ]

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