红外技术2024，Vol.46Issue(8)：883-891,9.
结合并行池化与自蒸馏的YOLO红外目标检测算法
YOLO Infrared Target Detection Algorithm Combining Parallel Pooling and Self-Distillation
摘要
Abstract
This study addresses low accuracy in infrared object detection,which is often caused by low contrast and scale discrepancies.A parallel-pooling and self-distillation-YOLO(PPSD-YOLO)algorithm that incorporates several crucial features was proposed to solve this challenge.One of the main contributions of this study is the Fusion-P parallel pooling module,which smoothes the surrounding pixels and avoids overlooking important details.In addition,a small infrared object detection layer was added to enhance the detection accuracy of such objects.The initialization anchor frame for this layer was optimized using the K-means++algorithm.The proposed algorithm includes a multiscale feature perception module(SA-RFE)in the neck layer,which fuses contextual information from various scales of the target for more accurate detection.During the training process,a modified self-distillation framework was used to rectify misdetected targets in the teacher model,leading to improved detection accuracy in the student model.Tests were conducted using the FLIR dataset to evaluate the proposed algorithm.The results show that PPSD-YOLO outperformed YOLOv7 by 2.7%in terms of mAP.This improvement can be attributed to the incorporation of a parallel pooling module,small-object detection layer,SA-RFE module,and a self-distillation framework.This study presents a comprehensive solution to the challenge of low detection accuracy in infrared object detection.The proposed PPSD-YOLO algorithm integrates advanced features that enhance accuracy and improve the overall performance of the detection system.These findings will be useful for researchers and practitioners in computer vision.关键词
红外目标检测/YOLO v7/自蒸馏/空洞卷积Key words
infrared target detection/YOLO v7/self distillation/dilated convolution分类
计算机与自动化引用本文复制引用
周华平,吴劲,李敬兆,吴涛..结合并行池化与自蒸馏的YOLO红外目标检测算法[J].红外技术,2024,46(8):883-891,9.基金项目
国家自然科学基金项目(52374154),安徽省重点研发计划国际科技合作专项(202004b11020029). （52374154）
