计算机技术与发展2025，Vol.35Issue(7)：16-23,8.DOI:10.20165/j.cnki.ISSN1673-629X.2025.0052
基于自动语义编辑的目标检测测试数据生成方法
Test Data Generation for Object Detection Based on Automated Semantic Editing
摘要
Abstract
Test data generation for object detection systems is crucial for evaluating model performance and identifying potential defects.Existing methods still have limitations in generating diverse and realistic data.We present SemaGen,a test data generation method for object detection based on automated semantic editing,which achieves advanced semantic operations such as insertion,deletion,and replacement through constructing high-quality semantic object libraries and combining automated editing strategies.Specifically,the proposed method first constructs a semantic object library through multiple screening mechanisms to ensure object semantic integrity and scene adaptability.Secondly,it utilizes a scene complexity quantification model that comprehensively considers background ratio,instance quantity,and spatial distribution to achieve adaptive selection of editing strategies.Finally,it proposes an object importance-based replacement strategy,an iterative deletion method,and an intelligent insertion mechanism considering semantic similarity to ensure the authenticity and diversity of generated images.The experimental results show that SemaGen significantly outperforms the existing methods on the three object manipulation tasks,generates higher quality images with better FID scores,and confirms its superiority in gen-erating data quality.In object detection model testing,SemaGen successfully identifies performance deficiencies of mainstream detectors such as YOLO v11,SSD,and Mask R-CNN in complex scenarios,providing new insights and tools for generating object detection test cases.关键词
目标检测/语义编辑/测试数据生成/深度神经网络/图像生成Key words
object detection/semantic editing/test data generation/deep neural networks/image generation分类
信息技术与安全科学引用本文复制引用
陈皓明,桂智明,刘艳芳,范鑫鑫,路云峰..基于自动语义编辑的目标检测测试数据生成方法[J].计算机技术与发展,2025,35(7):16-23,8.基金项目
复杂关键软件环境全国重点实验室自主课题(SKLSDE-2023ZX-17) （SKLSDE-2023ZX-17）
