{"title":"遥感图像中 YOLO 系列物体检测的一般优化方法","authors":"Guozheng Nan;Yue Zhao;Chengxing Lin;Qiaolin Ye","doi":"10.1109/LSP.2024.3469787","DOIUrl":null,"url":null,"abstract":"The You Only Look Once (YOLO) series of object detection algorithms has attracted considerable attention for its notable advantages in speed and accuracy, resulting in widespread applications in various real-world scenarios. However, achieving outstanding accuracy on remote sensing images with densely arranged small targets and complex backgrounds remains a challenging task. To address this issue, this letter proposes two easily integrated modules suitable for the YOLO architecture, namely global semantic information extraction (GSIE) and adaptive feature fusion (AFF). The GSIE module is designed to overcome the limitation of local information in traditional methods and facilitate global semantic information interaction by introducing multi-angle feature rotation to extend the receptive field. The AFF module effectively captures fine-grained features of objects by dynamically adjusting fusion weights, thereby reducing the loss of deep semantic information during feature transfer and fusion. The experimental results on the VEDAI and LEVIR remote sensing datasets demonstrate that when embedding these two modules into YOLO series algorithms that only use the small-scale detector, there is a significant improvement in performance while reducing computational complexity.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":null,"pages":null},"PeriodicalIF":3.2000,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"General Optimization Methods for YOLO Series Object Detection in Remote Sensing Images\",\"authors\":\"Guozheng Nan;Yue Zhao;Chengxing Lin;Qiaolin Ye\",\"doi\":\"10.1109/LSP.2024.3469787\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The You Only Look Once (YOLO) series of object detection algorithms has attracted considerable attention for its notable advantages in speed and accuracy, resulting in widespread applications in various real-world scenarios. However, achieving outstanding accuracy on remote sensing images with densely arranged small targets and complex backgrounds remains a challenging task. To address this issue, this letter proposes two easily integrated modules suitable for the YOLO architecture, namely global semantic information extraction (GSIE) and adaptive feature fusion (AFF). The GSIE module is designed to overcome the limitation of local information in traditional methods and facilitate global semantic information interaction by introducing multi-angle feature rotation to extend the receptive field. The AFF module effectively captures fine-grained features of objects by dynamically adjusting fusion weights, thereby reducing the loss of deep semantic information during feature transfer and fusion. The experimental results on the VEDAI and LEVIR remote sensing datasets demonstrate that when embedding these two modules into YOLO series algorithms that only use the small-scale detector, there is a significant improvement in performance while reducing computational complexity.\",\"PeriodicalId\":13154,\"journal\":{\"name\":\"IEEE Signal Processing Letters\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2024-09-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Signal Processing Letters\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10697274/\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Signal Processing Letters","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10697274/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
General Optimization Methods for YOLO Series Object Detection in Remote Sensing Images
The You Only Look Once (YOLO) series of object detection algorithms has attracted considerable attention for its notable advantages in speed and accuracy, resulting in widespread applications in various real-world scenarios. However, achieving outstanding accuracy on remote sensing images with densely arranged small targets and complex backgrounds remains a challenging task. To address this issue, this letter proposes two easily integrated modules suitable for the YOLO architecture, namely global semantic information extraction (GSIE) and adaptive feature fusion (AFF). The GSIE module is designed to overcome the limitation of local information in traditional methods and facilitate global semantic information interaction by introducing multi-angle feature rotation to extend the receptive field. The AFF module effectively captures fine-grained features of objects by dynamically adjusting fusion weights, thereby reducing the loss of deep semantic information during feature transfer and fusion. The experimental results on the VEDAI and LEVIR remote sensing datasets demonstrate that when embedding these two modules into YOLO series algorithms that only use the small-scale detector, there is a significant improvement in performance while reducing computational complexity.
期刊介绍:
The IEEE Signal Processing Letters is a monthly, archival publication designed to provide rapid dissemination of original, cutting-edge ideas and timely, significant contributions in signal, image, speech, language and audio processing. Papers published in the Letters can be presented within one year of their appearance in signal processing conferences such as ICASSP, GlobalSIP and ICIP, and also in several workshop organized by the Signal Processing Society.