针对复杂背景下低分辨率舰船目标的改进YOLOv7算法

Improved YOLOv7 Algorithm for Low-resolution Ship Object Detection in Complex Backgrounds

针对舰船图像目标检测中对于复杂背景下低分辨率目标检测易受干扰、检测精度低的问题,提出一种改进的YOLOv7算法用于识别舰船目标。主要在3个方面对算法进行改进,分别为:在舰船目标数据集中使用K-means++算法进行锚框聚类,得到更适合舰船检测任务的的锚框信息;改进损失函数,使用EIOU损失代替CIOU损失,使用与ɑ-Balanced结合的Focal loss代替标准交叉熵损失;改进网络结构,增加SPD-Conv模块,提升对于低分辨率目标的检测效果。实验结果表明,改进后的YOLOv7算法与原始的YOLOv7算法相比,精度提升了4.22个百分点,召回率提升了2.68个百分点,mAP@0.5提升了4.3个百分点,检测速度提升了2帧/s,对舰船目标达到了良好的检测效果。

In response to the problems of low resolution target detection and interference from complex backgrounds in ship image target detection,an improved YOLOv7 algorithm is proposed for identifying ship targets.The algorithm is mainly improved in three aspects:using K-means++algorithm for anchor box clustering in the ship target dataset to obtain anchor box information that is more suitable for ship detection tasks;improving the loss function by using EIOU loss instead of CIOU loss and using Focal loss combined withɑ-Balanced instead of standard cross-entropy loss;improving the network structure by adding the SPD-Conv module to enhance the detection effect for low-resolution targets.Experimental results show that compared with the original YOLOv7 algorithm,the improved algorithm has an accuracy improvement of 4.22 percentage points,a recall rate improvement of 2.68 percentage points,a mAP@0.5 improvement of 4.3 percentage points,and a detection speed improvement of 2 frames/s.The algorithm achieves good detection results for ship targets.