{"title":"DEPDet:用于复杂场景中 SAR 图像船舶检测的跨空间多尺度轻量级网络","authors":"Jing Zhang;Fan Deng;Yonghua Wang;Jie Gong;Ziyang Liu;Wenjun Liu;Yinmei Zeng;Zeqiang Chen","doi":"10.1109/JSTARS.2024.3469209","DOIUrl":null,"url":null,"abstract":"Nowadays, the intricate nature of synthetic aperture radar (SAR) ship scenes, coupled with the presence of multiscale targets, poses a significant challenge in detection accuracy. Furthermore, to reduce the financial outlay on hardware, there is also a considerable challenge in lightweighting the model. In order to resolve the aforementioned concerns, we propose a cross-spatial multiscale lightweight network, designated as DEPDet. First, a new efficient multiscale detection backbone network DEMNet is redesigned. To improve the feature extraction capability of the network, a cross-spatial multiscale convolution (CSMSConv) is designed and a new CSMSConv module CSMSC2F is constructed. Meanwhile, we introduce an efficient multiscale attention module. DEMNet is capable of more effectively extracting information pertaining to multiscale ships. Moreover, to enhance the fusion of features at diverse scales, we design a new path aggregation feature pyramid network DEPAFPN, which combines deformable convolution and CSMSC2F. Finally, we introduce partial convolution to construct a lightweight detection head module PCHead, which can be employed to extract spatial features with greater efficiency. The publicly available SAR ship datasets, SAR Ship Detection Dataset and High-Resolution SAR Image Dataset, are employed for the purpose of conducting experiments. The mean average precision (mAP) obtained was 98.2% (+1.4%) and 91.6% (+1.6%), respectively. The F1 obtained 0.950 (+1.7%) and 0.871 (+1.4%), respectively. Concurrently, the Params decreased from 3.2M to 2.1M, a decrease of approximately 34%. The floating-point operations (FLOPs) decreased from 8.7G to 4.5G, a decrease of approximately 48%. The experimental results indicate that the network achieves an effective balance between detection accuracy and lightweight effect with good generalization and extensibility.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10695810","citationCount":"0","resultStr":"{\"title\":\"DEPDet: A Cross-Spatial Multiscale Lightweight Network for Ship Detection of SAR Images in Complex Scenes\",\"authors\":\"Jing Zhang;Fan Deng;Yonghua Wang;Jie Gong;Ziyang Liu;Wenjun Liu;Yinmei Zeng;Zeqiang Chen\",\"doi\":\"10.1109/JSTARS.2024.3469209\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Nowadays, the intricate nature of synthetic aperture radar (SAR) ship scenes, coupled with the presence of multiscale targets, poses a significant challenge in detection accuracy. Furthermore, to reduce the financial outlay on hardware, there is also a considerable challenge in lightweighting the model. In order to resolve the aforementioned concerns, we propose a cross-spatial multiscale lightweight network, designated as DEPDet. First, a new efficient multiscale detection backbone network DEMNet is redesigned. To improve the feature extraction capability of the network, a cross-spatial multiscale convolution (CSMSConv) is designed and a new CSMSConv module CSMSC2F is constructed. Meanwhile, we introduce an efficient multiscale attention module. DEMNet is capable of more effectively extracting information pertaining to multiscale ships. Moreover, to enhance the fusion of features at diverse scales, we design a new path aggregation feature pyramid network DEPAFPN, which combines deformable convolution and CSMSC2F. Finally, we introduce partial convolution to construct a lightweight detection head module PCHead, which can be employed to extract spatial features with greater efficiency. The publicly available SAR ship datasets, SAR Ship Detection Dataset and High-Resolution SAR Image Dataset, are employed for the purpose of conducting experiments. The mean average precision (mAP) obtained was 98.2% (+1.4%) and 91.6% (+1.6%), respectively. The F1 obtained 0.950 (+1.7%) and 0.871 (+1.4%), respectively. Concurrently, the Params decreased from 3.2M to 2.1M, a decrease of approximately 34%. The floating-point operations (FLOPs) decreased from 8.7G to 4.5G, a decrease of approximately 48%. The experimental results indicate that the network achieves an effective balance between detection accuracy and lightweight effect with good generalization and extensibility.\",\"PeriodicalId\":13116,\"journal\":{\"name\":\"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2024-09-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10695810\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10695810/\",\"RegionNum\":2,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10695810/","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
DEPDet: A Cross-Spatial Multiscale Lightweight Network for Ship Detection of SAR Images in Complex Scenes
Nowadays, the intricate nature of synthetic aperture radar (SAR) ship scenes, coupled with the presence of multiscale targets, poses a significant challenge in detection accuracy. Furthermore, to reduce the financial outlay on hardware, there is also a considerable challenge in lightweighting the model. In order to resolve the aforementioned concerns, we propose a cross-spatial multiscale lightweight network, designated as DEPDet. First, a new efficient multiscale detection backbone network DEMNet is redesigned. To improve the feature extraction capability of the network, a cross-spatial multiscale convolution (CSMSConv) is designed and a new CSMSConv module CSMSC2F is constructed. Meanwhile, we introduce an efficient multiscale attention module. DEMNet is capable of more effectively extracting information pertaining to multiscale ships. Moreover, to enhance the fusion of features at diverse scales, we design a new path aggregation feature pyramid network DEPAFPN, which combines deformable convolution and CSMSC2F. Finally, we introduce partial convolution to construct a lightweight detection head module PCHead, which can be employed to extract spatial features with greater efficiency. The publicly available SAR ship datasets, SAR Ship Detection Dataset and High-Resolution SAR Image Dataset, are employed for the purpose of conducting experiments. The mean average precision (mAP) obtained was 98.2% (+1.4%) and 91.6% (+1.6%), respectively. The F1 obtained 0.950 (+1.7%) and 0.871 (+1.4%), respectively. Concurrently, the Params decreased from 3.2M to 2.1M, a decrease of approximately 34%. The floating-point operations (FLOPs) decreased from 8.7G to 4.5G, a decrease of approximately 48%. The experimental results indicate that the network achieves an effective balance between detection accuracy and lightweight effect with good generalization and extensibility.
期刊介绍:
The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society. The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations. The ‘Applications’ areas encompasses the societal benefit areas of the Global Earth Observations Systems of Systems (GEOSS) program. Through deliberations over two years, ministers from 50 countries agreed to identify nine areas where Earth observation could positively impact the quality of life and health of their respective countries. Some of these are areas not traditionally addressed in the IEEE context. These include biodiversity, health and climate. Yet it is the skill sets of IEEE members, in areas such as observations, communications, computers, signal processing, standards and ocean engineering, that form the technical underpinnings of GEOSS. Thus, the Journal attracts a broad range of interests that serves both present members in new ways and expands the IEEE visibility into new areas.