面向小目标的自校正YOLOv5检测增强算法OACSTPCD
Self-calibrated YOLOv5 Detection Enhancement Algorithm for Small Targets
为了解决汽车智能驾驶精度不够高、小目标漏检和误检的问题,提出了一种基于自校正卷积的YOLOv5s的道路目标检测算法.该算法主要设计了一种自校正卷积网络,通过深层特征提取以及特征融合来提高检测精度以及对小目标的检测能力.对自校正卷积网络进行轻量化处理,减少模型的大小以及训练过程中的参数量.增加小目标校正检测层,输出检测小目标的特征图.设计了SAIoU损失函数来代替目标框回归中的CIoU损失函数,加速目标框的回归.在公开的自动驾驶KITTI数据集和B…查看全部>>
In order to solve the problems of low precision of intelligent driving and missing and false detection of small targets,a road target detection algorithm based on self-calibrated convolutional YOLOv5s is proposed.In this algorithm,a self-calibrated convolutions network is designed to improve the detection accuracy and detection ability of small targets through deep feature extraction and feature fusion.Lightweight processing is applied to the self-calibrated…查看全部>>
陈钧;周井泉;张志鹏
南京邮电大学 电子与光学工程学院、柔性电子学院,江苏 南京 210023南京邮电大学 电子与光学工程学院、柔性电子学院,江苏 南京 210023南京邮电大学 电子与光学工程学院、柔性电子学院,江苏 南京 210023
计算机与自动化
智能驾驶自校正卷积YOLOv5s轻量化小目标检测
intelligent drivingself-calibrated convolutionsYOLOv5slightweightsmall targets detection
《计算机技术与发展》 2024 (10)
140-147,8
国家自然科学基金(61401225)
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