基于YOLOv4算法的集装箱破损检测方法

Container damage detection method based on YOLOv4 algorithm

针对港口集装箱破损检测的算法较少,并且存在检测速度慢、检测精度低的问题,本文提出一种基于改进的YOLOv4卷积神经网络的集装箱破损检测方法。通过改进的K均值聚类算法获取集装箱数据集的锚点框,结合焦点分类损失函数,减少易分类样本的损失;引入α平衡因子调节正负样本的不均衡,使检测结果更加精确。实验结果表明,改进后的YOLOv4算法比目前流行的算法在明显破损检测及小目标破损检测上具有更好的效果,且不会明显增加检测时间,在集装箱破损检测等方面具有较高的实用价值。

There are few algorithms for port container damage detection,and there are problems of slow detection speed and low detection accuracy.Aimed at the problems,an improved YOLOv4 convolutional neural network is proposed for container damage detection.The anchor frame of the container dataset is obtained by an improved K-means clustering algorithm,and the focus classification loss function is combined to reduce the loss of easily classification samples.Theαbalance factor is introduced to adjust the imbalance between positive and negative samples,which makes the detection results more accurate.The experimental results show that,compared with the current popular algorithm,the improved YOLOv4 algorithm is of better effect on obvious damage detection and small target damage detection,and the detection time does not significantly increase,so it has a higher practical value in container damage detection.