基于自适应切片辅助推理的航拍图像目标检测方法  

Object detection method for aerial images based on adaptive slicing aided inference

作　　者：金黎威 徐望明[1,2] 李垚翔 JIN Liwei;XU Wangming;LI Yaoxiang(School of Information Science and Engineering,Wuhan University of Science and Technology,Wuhan 430081,China;Engineering Research Center for Metallurgical Automation and Detecting Technology of Ministry of Education,Wuhan University of Science and Technology,Wuhan 430081,China)

机构地区：[1]武汉科技大学信息科学与工程学院,湖北武汉430081 [2]武汉科技大学冶金自动化与检测技术教育部工程研究中心,湖北武汉430081

出　　处：《液晶与显示》2025年第3期472-480,共9页Chinese Journal of Liquid Crystals and Displays

基　　金：国家自然科学基金(No.51805386);冶金自动化与检测技术教育部工程研究中心开放课题(No.MADTOF2021B02)。

摘　　要：针对无人机航拍图像中目标尺度不一、密度不一、细节不清,尤其小目标众多所导致的漏检和误检问题,提出一种基于自适应切片辅助推理的目标检测新方法。该方法首先将航拍图像输入目标检测网络进行初次推理,设计一种窗口得分机制来根据初次推理结果定位输入图像中的不确定目标,并自动选择有效的图像区域进行切片以适应不同尺度和密度的目标。接着将切片图像送入目标检测网络进行二次推理。最后对两次推理结果执行改进的非极大值抑制处理得到最终检测结果。在典型的VisDrone2019和AI-TOD数据集上的实验结果表明,本文方法提升了包括YOLOv7-tiny、YOLOv8n、YOLOv8s及YOLOv9-C在内的典型轻量级目标检测模型的mAP指标,有效提高了航拍图像目标检测性能。In order to address the problem of missed and false detections caused by different object scales,densities,unclear details and especially numerous small objects in drone aerial images,a novel object detection method based on Adaptive Slicing Aided Inference(ASAI)is proposed.Firstly,an aerial image is input into an object detection network for initial inference.With the initial inference results,a window scoring mechanism is designed to locate the ambiguous targets in the input image,and the effective image regions are selected automatically for slicing to adapt to objects with different scales and densities.Then,the sliced images are sent to the object detection network for secondary inference.Finally,the two inference results are processed by an improved non-maximum suppression(NMS)algorithm to obtain the final detection result.Experimental results on typical datasets of VisDrone2019 and AI-TOD indicate that the proposed method improves the mAP metrics of typical lightweight object detection models including YOLOv7-tiny,YOLOv8n,YOLOv8s and YOLOv9-C,effectively improving the performance of object detection for aerial images.

关 键 词：小目标检测 自适应切片辅助推理 不确定目标定位 非极大值抑制 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]