基于改进YOLO V3的舰船目标检测算法

A Ship Target Detection Algorithm Based on Improved YOLO V3

针对YOLO V3算法中对于小目标检测精度不高、容易出现漏检误检的问题,提出了一种基于改进YOLO V3的舰船目标检测算法。首先,通过在YOLO V3原网络结构基础上额外从主干网络引出一个输出尺度,将其与上一个输出尺度中的特征信息进行特征拼接,得到具有更丰富语义信息的特征向量;其次,基于数据集进行聚类改进,改进度量距离公式、重新设置anchor box的个数与相应参数;最后,优化改进YOLO V3的损失函数,提高模型的整体性能。对测试数据集进行分析实验,结果表明改进后的检测算法平均精确度达到83.98%,较之于原YOLO V3,平均精确度提升了6.72%。

The original YOLO V3 algorithm has low accuracy in small target detection and is prone to missed detection and false detection.To solve the problema ship target detection algorithm based on the improved YOLO V3 is proposed.Firstlybased on the structure of the original YOLO V3 networkan additional output scale is derived from the backbone networkwhose feature information is spliced with that of the prior output scaleso as to obtain a feature vector with richer semantic information.Secondlybased on the data setthe clustering is improvedin which process the distance measurement formula is improvedand the number of anchor boxes and the corresponding parameters are reset.Finallythe loss function of the improved YOLO V3 is optimizedso as to improve the overall performance of the model.Analysis and experimental results on the test data set show that the average detection accuracy of the improved algorithm is 83.98%which is 6.72%higher than that of the original YOLO V3.