重庆工商大学学报（自然科学版）2025，Vol.42Issue(3)：102-109,8.DOI:10.16055/j.issn.1672-058X.2025.0003.013
基于上下文增强和特征融合的目标检测算法
Target Detection Algorithm Based on Context Enhancement and Feature Fusion
摘要
Abstract
Objective Aiming at the issue of inadequate characterization capacity of CenterNet anchor-free object recognition algorithm,a method based on context enhancement and feature fusion was proposed.Methods This method adopted the concepts of multi-receptive field and information fusion to construct an adaptive context extraction module and feature fusion strategy.Firstly,the network obtained the contextual features of the target through the multipath dilated convolution of the adaptive context extraction module,prompting deep networks to learn multi-scale information.Then,nonlinear factors were added to the network through the ACON-C activation function,adaptively activating the neurons of the network and enhancing the data-fitting ability of the network.Finally,a joint attention feature fusion strategy was used to merge feature information at different levels.By integrating semantic features from the high-level network and positional features from the low-level network,the feature information that is useful for the recognition task was captured.At the same time,the correlation between the feature maps in multiple levels of channels was learned to enhance the network's focus on key target features.Results The proposed method achieved an mAP of 83.82％on the PASCAL VOC public dataset,an improvement of 3.72％compared with the CenterNet baseline algorithm.It also outperformed classic algorithms such as Faster R-CNN,SSD,and YOLOv3 by 7.4％,9.5％,and 3.5％,respectively.Conclusion The proposed method effectively enhances the detection performance of the CenterNet algorithm,and the improved CenterNet has higher recognition accuracy compared with other target recognition algorithms.The proposed method proves to be practical in target detection applications,validating the effectiveness of the proposed approach.关键词
目标检测/空洞卷积/上下文特征/特征融合/注意力机制/ACON激活函数Key words
target detection/dilated convolution/context feature/feature fusion/attention mechanism/ACON activation function分类
信息技术与安全科学引用本文复制引用
杨海燕,王凤随,张兴旺..基于上下文增强和特征融合的目标检测算法[J].重庆工商大学学报（自然科学版）,2025,42(3):102-109,8.基金项目
安徽省自然科学基金项目(2108085MF197) （2108085MF197）
安徽高校省级自然科学研究重点项目(KJ2019A0162) （KJ2019A0162）
安徽工程大学国家自然科学基金预研项目(XJKY2022040). （XJKY2022040）
