Detecting small objects in unmanned aerial vehicle(UAV)imagery is a challenging and crucial task in computer vision.Most current methods struggle to address the challenges of small objects:fine-grained feature mining,...Detecting small objects in unmanned aerial vehicle(UAV)imagery is a challenging and crucial task in computer vision.Most current methods struggle to address the challenges of small objects:fine-grained feature mining,multiple-layer feature fusion,and mismatches in scale between anchors and feature maps.To alleviate the aforementioned issues,we present FGHDet,which focuses on delving into fine-grained features in low-level features with a head selection mechanism.First,our approach introduces a detail-preserving semantic information enhancement module(DSIEM)to retain fine-grained information while excavating coarse-grained semantic details relevant to fine-grained information.Then,we devise a coarse-to-fine feature guidance module(CFGM)that leverages coarse-grained semantic information and finegrained information to co-guide feature enhancement,further improving the model's classification ability.Finally,we introduce a multiscale detection strategy based on anchor-head matching,ensuring scale-level matching between anchors and feature maps to prevent overfitting due to overly fine anchor divisions.Extensive experiments on the VisDrone,CARPK,and Drone-vs.-Bird datasets demonstrate that FGHDet achieves notable improvements in mAP(IoU range[0.5:0.95])of 4.9,4.1,and 2.2,respectively.The code is available at https://github.com/b-yanchao/UAVDetection.git.展开更多
基金partially supported by the Major Basic Research Project of Shandong Provincial Natural Science Foundation under Grant No.ZR2024ZD03the National Natural Science Foundation of China under Grant No.62176141+4 种基金the Taishan Scholar Project of Shandong Province under Grant No.tsqn202103088the Major Science and Technology Innovation Project of Shandong Province of China under Grant No.2021CXGC11204the Natural Science Foundation of Shandong Province of China under Grant No.ZR202103010201the Shandong Excellent Young Scientists Fund under Grant No.ZR2024YQ006the Shandong Province Higher Education Institutions Youth Entrepreneurship and Technology Support Program under Grant No.2023KJ027.
文摘Detecting small objects in unmanned aerial vehicle(UAV)imagery is a challenging and crucial task in computer vision.Most current methods struggle to address the challenges of small objects:fine-grained feature mining,multiple-layer feature fusion,and mismatches in scale between anchors and feature maps.To alleviate the aforementioned issues,we present FGHDet,which focuses on delving into fine-grained features in low-level features with a head selection mechanism.First,our approach introduces a detail-preserving semantic information enhancement module(DSIEM)to retain fine-grained information while excavating coarse-grained semantic details relevant to fine-grained information.Then,we devise a coarse-to-fine feature guidance module(CFGM)that leverages coarse-grained semantic information and finegrained information to co-guide feature enhancement,further improving the model's classification ability.Finally,we introduce a multiscale detection strategy based on anchor-head matching,ensuring scale-level matching between anchors and feature maps to prevent overfitting due to overly fine anchor divisions.Extensive experiments on the VisDrone,CARPK,and Drone-vs.-Bird datasets demonstrate that FGHDet achieves notable improvements in mAP(IoU range[0.5:0.95])of 4.9,4.1,and 2.2,respectively.The code is available at https://github.com/b-yanchao/UAVDetection.git.
