基于深度学习的复杂场景目标检测与识别算法优化研究  

Research on optimization of complex scene object detection and recognition algorithm based on deep learning

作　　者：时薇 SHI Wei(Hebei University of Communication,Shijiazhuang 050000,China)

机构地区：[1]河北传媒学院,石家庄050000

出　　处：《计算机应用文摘》2025年第8期93-95,98,共4页

基　　金：2023年度河北省教育科学“十四五”规划立项课题:新一代信息技术条件下高校教学场景建设与管理研究(2304146)。

摘　　要：目标检测与识别是计算机视觉中的关键技术,但在实际应用中常面临光照变化、遮挡和多目标等复杂场景的挑战。对此,文章深入研究了算法优化策略,分析了复杂场景对检测与识别的影响以及传统算法的局限性。通过数据增强、特征提取优化和模型结构改进等策略,特别是利用图像变换模拟复杂场景特征的数据增强方法,显著提升了算法在复杂场景下的性能。实验结果表明,优化后的算法在多个代表性数据集上实现了更高的准确率、召回率、F1分数和平均精度,同时保持了较高的处理速度,这说明优化策略有效应对了复杂场景的挑战,显著提升了目标检测与识别算法的性能。Object detection and recognition are key technologies in computer vision,but they often face challenges in complex scenes such as lighting changes,occlusion,and multiple targets in practical applications.In this regard,this article delves into algorithm optimization strategies,analyzes the impact of complex scenarios on detection and recognition,and the limitations of traditional algorithms.Through strategies such as data augmentation,feature extraction optimization,and model structure improvement,especially the use of image transformation to simulate complex scene features in data augmentation methods,the performance of the algorithm in complex scenes has been significantly improved.The experimental results show that the optimized algorithm achieves higher accuracy,recall,F1 score,and average precision on multiple representative datasets,while maintaining a high processing speed.This indicates that the optimization strategy effectively addresses the challenges of complex scenarios and significantly improves the performance of target detection and recognition algorithms.

关 键 词：深度学习 复杂场景 目标检测与识别 算法优化 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]