{"title":"DET-YOLO:在遥感图像中探测军用飞机的创新型高性能模型","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. 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引用次数: 0
摘要
为了解决目前复杂遥感数据中军用飞机探测率低、漏检率高的难题,同时满足实时探测和模型易于部署的要求,本文介绍了一种创新的探测模型 DET-you only look once (YOLO)。首先,为了解决在错综复杂的背景中识别小目标的准确性降低的问题,我们设计了一个新颖的特征提取组件 C2f_DEF。该模块取代了 YOLOv8n 中现有的所有 C2f 组件,从而大大增强了模型应对复杂环境背景的能力。其次,为了实现模型易于部署的功能,简化了一些深层结构,使模型更加轻便。之后,为了进一步提高模型处理遥感图像中复杂背景和密集环境的能力,提高模型对军用飞机的探测精度,在模型中嵌入了 DAT 模块。最后,本文还优化了损失函数和 reg_max,以进一步降低计算成本,同时提高模型的探测精度。为了验证 DET-YOLO 的有效性和强大的通用性,我们在 MAR20、NWPU VHR-10 和 NEU-DET 这三个公开数据集上进行了广泛的实验验证。在 MAR20 数据集上,与其他先进模型相比,DET-YOLO 仅用了 80 个训练历元就达到了最高的 mAP0.5(即 94.7%),同时满足了轻量级和实时性要求。在其他两个数据集上,DET-YOLO 也取得了最佳检测性能。
DET-YOLO: An Innovative High-Performance Model for Detecting Military Aircraft in Remote Sensing Images
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.