期刊论文

中文

激光杂志

0253-2743

2024

45

2

36-42

10.14016/j.cnki.jgzz.2024.2.036

No.62101204:国家自然科学基金 No.2020CFB474:湖北省自然科学基金

本校为第一机构

小目标检测是计算机视觉领域具有挑战性的研究任务.针对小目标物体尺寸小、特征不明显、目标聚集等问题,提出了 一种基于自适应特征融合和任务对齐的小目标检测算法C-SODNET.该算法在TOOD基础上进行优化与改进,引入ConvNeXt作为骨干网络,通过嵌入CBAM注意力机制和自适应特征融合模块的特征金字塔结构提升兴趣区域的特征提取能力,同时在检测头加入可变形卷积,显著改善了对于小目标物体的检测能力,最后引入CIoU回归损失函数来训练模型.实验结果表明,C-SODNET在VisDrone2019小目标检测数据集mAP50为51.2％,相较于TOOD算法准确率提升了 9.4％,小目标物体的精确率APs提高了 7.3％,验证了算法的有效性...

Small target detection is a challenging research task in the field of computer vision.For the problems of small target object size,inconspicuous features and target aggregation,a small target detection algorithm C-SODNET based on adaptive feature fusion and task alignment is proposed.The algorithm is optimized and improved on the basis of TOOD by introducing ConvNeXt as the backbone network,improving the feature pyramid structure by embedding CBAM attention mechanism and adaptive feature fusion module feature extraction capability of the region...