{"title":"DET-YOLO: An Innovative High-Performance Model for Detecting Military Aircraft in Remote Sensing Images","authors":"Xiaoxin Chen;Hui Jiang;Hongxin Zheng;Jiankun Yang;Riqiang Liang;Dan Xiang;Hao Cheng;Zhansi Jiang","doi":"10.1109/JSTARS.2024.3462745","DOIUrl":null,"url":null,"abstract":"To address the challenges of low detection rate and high missed detection rate of military aircraft in current complex remote sensing data, and to meet the requirements of real-time detection and easy deployment of models, this article introduces DET-you only look once (YOLO), an innovative detection model. First, to tackle the issue of reduced accuracy in identifying small targets amidst intricate backgrounds, a novel feature extraction component, C2f_DEF, was devised. This module replaced all existing C2f components within YOLOv8n, thereby significantly enhancing the model's ability to cope with complicated environmental contexts. Second, to achieve the functionality of easy deployment of the model, some deep structures were simplified to make the model more lightweight. Afterward, to further improve the model's ability to handle complex backgrounds and dense environments in remote sensing images and to improve the model's detection accuracy for military aircraft, the DAT module was embedded in the model. Finally, this article also optimized the loss function and reg_max to further reduce computational costs while improving the detection accuracy of the model. To verify the effectiveness and strong universality of DET-YOLO, extensive experimental verification was conducted on three publicly available datasets, namely MAR20, NWPU VHR-10, and NEU-DET. On the MAR20 dataset, compared with other advanced models, DET-YOLO achieved the highest mAP\n<sub>0.5</sub>\n (namely 94.7%) with only 80 training epochs while meeting lightweight and real-time requirements. While on the other two datasets, DET-YOLO also achieved the best detection performance.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10681280","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/10681280/","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}
引用次数: 0
Abstract
To address the challenges of low detection rate and high missed detection rate of military aircraft in current complex remote sensing data, and to meet the requirements of real-time detection and easy deployment of models, this article introduces DET-you only look once (YOLO), an innovative detection model. First, to tackle the issue of reduced accuracy in identifying small targets amidst intricate backgrounds, a novel feature extraction component, C2f_DEF, was devised. This module replaced all existing C2f components within YOLOv8n, thereby significantly enhancing the model's ability to cope with complicated environmental contexts. Second, to achieve the functionality of easy deployment of the model, some deep structures were simplified to make the model more lightweight. Afterward, to further improve the model's ability to handle complex backgrounds and dense environments in remote sensing images and to improve the model's detection accuracy for military aircraft, the DAT module was embedded in the model. Finally, this article also optimized the loss function and reg_max to further reduce computational costs while improving the detection accuracy of the model. To verify the effectiveness and strong universality of DET-YOLO, extensive experimental verification was conducted on three publicly available datasets, namely MAR20, NWPU VHR-10, and NEU-DET. On the MAR20 dataset, compared with other advanced models, DET-YOLO achieved the highest mAP
0.5
(namely 94.7%) with only 80 training epochs while meeting lightweight and real-time requirements. While on the other two datasets, DET-YOLO also achieved the best detection performance.
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.