自动色阶与双向特征融合的水下目标检测算法

Underwater Target Detection Algorithm Based on Automatic Color Level and Bidirectional Feature Fusion

水下环境存在光线差、噪声大等复杂情况,导致传统水下目标检测方法检测精度较低、漏检率较高.针对上述问题,在现阶段通用的Faster R-CNN的基础上,提出一种自动色阶与双向特征融合的水下目标检测算法.首先,采用自动色阶对水下模糊图像进行增强处理;其次,采用PAFPN进行双向特征融合,以增强对浅层信息的表达能力;然后,在训练前后均引入柔性非极大值抑制(Soft-NMS)算法,来修正并生成候选目标区域;最后,采用FocalLoss函数,解决正负样本分配不均衡的问题.实验结果表明,所提算法在URPC2020数据集上的检测准确率可达59.7%,召回率可达70.5%,相比现阶段通用的Faster R-CNN算法,分别提高了 5.5个百分点和8.4个百分点,有效提高了水下目标检测的准确率.

Many complex elements such as poor light and high noise in the underwater environment result in low detection accuracy and high missed detection rate in traditional underwater target detection methods.To address these issues,based on the current general Faster RCNN algorithm,this study proposes an underwater target detection algorithm based on automatic color level and bidirectional feature fusion.First,the automatic color level was used to enhance a blurred underwater image.Second,the path aggregation feature pyramid network(PAFPN)was introduced for feature fusion to enhance the expression for shallow information.Third,the soft nonmaximum suppression(SoftNMS)algorithm was introduced to modify and generate the candidate target regions before and after training.Finally,the FocalLoss function was used to rectify the issue of an unbalanced distribution of positive and negative samples.The experimental results show that the proposed algorithm can reach a detection accuracy of 59.7%on the URPC2020 dataset and a recall rate of 70.5%,which are 5.5 percentage points and 8.4 percentage points respectively higher than the current general Faster RCNN algorithm,effectively improving the average accuracy of underwater target detection.