红外技术2024，Vol.46Issue(5)：565-575,11.
基于DCS-YOLOv8模型的红外图像目标检测方法
Infrared Image Object Detection Method Based on DCS-YOLOv8 Model
摘要
Abstract
In response to the challenges posed by low signal-to-noise ratios and complex task scenarios,an improved detection method called DCS-YOLOv8(DCN_C2f-CA-SIoU-YOLOv8)is proposed to address the insufficient infrared occluded object detection and weak target detection capabilities of the YOLOv8 model.Building on the YOLOv8 framework,the backbone network incorporates a lightweight deformable convolution network(DCN_C2f)module based on deformable convolutions,which adaptively adjusts the network's visual receptive field to enhance the multi-scale feature representation of objects.The feature fusion network introduces the coordinate attention(CA)module based on coordinate attention mechanisms to capture spatial dependencies among multiple objects,thereby improving the object localization accuracy.Additionally,the position regression loss function is enhanced using Scylla IoU to ensure a relative displacement direction match between the predicted and ground truth boxes.This improvement accelerates the model convergence speed and enhances the detection and localization accuracy.The experimental results demonstrate that DCS-YOLOv8 achieves significant improvements in the average precision of the FLIR,OTCBVS,and VEDAI test sets compared to the YOLOv8-n\s\m\l\x series models.Specifically,the average mAP@0.5 values are enhanced by 6.8%,0.6%,and 4.0%respectively,reaching 86.5%,99.0%,and 75.6%.Furthermore,the model's inference speed satisfies the real-time requirements for infrared object detection tasks.关键词
红外图像/目标检测/注意力机制/可变形卷积/多尺度特征Key words
infrared images/object detection/attention mechanism/deformable convolution/multi-scale features分类
信息技术与安全科学引用本文复制引用
沈凌云,郎百和,宋正勋,温智滔..基于DCS-YOLOv8模型的红外图像目标检测方法[J].红外技术,2024,46(5):565-575,11.基金项目
山西省引进人才科技创新启动基金(21010123) （21010123）
山西省高等院校大学生创新项目(S202314101195) （S202314101195）
吉林省科技发展计划基金项目(YDZJ202102CXJD007). （YDZJ202102CXJD007）
