井冈山大学学报（自然科学版）2023，Vol.44Issue(6)：75-83,9.DOI:10.3969/j.issn.1674-8085.2023.06.010
基于多分支空洞卷积与自适应特征融合的FCOS目标检测算法
FCOS OBJECT DETECTION ALGORITHM BASED ON MULTI-BRANCH ATROUS CONVOLUTION AND ADAPTIVE FEATURE FUSION
摘要
Abstract
Object detection technology based on deep learning has been widely used in the fields of autonomous driving and robot vision.For this task,FCOS(fully convolutional one-stage object detection)uses full convolution and anchor-free method to achieve pixel-by-pixel object detection,but the original FCOS still has the problems as insufficient image feature extraction,insufficient global feature information acquisition and unsatisfactory feature fusion.Therefore,this paper improves FCOS and applies it to image multi-object detection.First,this paper uses ResNeSt50 instead of the original backbone ResNet50 to improve the feature extraction capability of the backbone by combining feature-map attention and multi-path representation.Then,a Receptive Field Enhancement Module(RFEM)is constructed based on multi-branch dilated convolutions to obtain more comprehensive global context information.Finally,based on the original FCOS feature fusion,this paper designs an Adaptive Recombination Feature Fusion Module(ARFFM),which efficiently fuses the semantic information of high-level feature maps and the detail information of low-level feature maps.Experiments on the PASCAL VOC2007 dataset show that the improved FCOS achieves a mean precision mean(mAP)of 81.2％,a 2.9％improvement over the original FCOS algorithm,and exhibits state-of-the-art performance on most classes.At the same time,extensive ablation experiments are carried out in this paper,in which the ResNeSt50,RFEM,and ARFFM modules bring 1.2％,2.1％,and 2.9％of the baseline network respectively,these improvements provide a new solution for the detection of small objects and occluded objects.关键词
目标检测/无锚方法/主干网络/空洞卷积/特征融合Key words
object detection/anchor free method/backbone/atrous convolution/feature fusion分类
信息技术与安全科学引用本文复制引用
刘糠继,时培成,齐恒,杨爱喜..基于多分支空洞卷积与自适应特征融合的FCOS目标检测算法[J].井冈山大学学报（自然科学版）,2023,44(6):75-83,9.基金项目
国家自然科学基金面上项目(51575001) （51575001）
安徽省重点研究与开发计划项目(202104a05020003) （202104a05020003）
安徽省自然科学基金项目(2208085MF173) （2208085MF173）
