基于改进RT-DETR的浅水海洋生物识别方法

Marine life identification method based on improved RT-DETR

针对现有浅水海洋生物识别方法在水下环境中对浅水海洋生物识别效果不佳的问题,提出了一种以RT-DETR为基准模型的改进浅水海洋生物识别方法。首先,使用重参数化网络RepViT作为模型的主干网络,提升模型的特征提取能力。然后,构建基于重参数化的并行膨胀卷积RepPDC并引入颈部网络中,使模型能够有效获取长距离上下文信息,有利于提升模型的识别精度。最后,基于注意力机制构建了双向特征融合模块CAFM,提升模型在水下环境中对重点信息的关注能力。实验结果表明,改进后的方法,mAP50提升至87.5%,mAP75提升至70.9%,mAP50:95提升至64.9%,且参数量更少,有望应用到实际浅水海洋生物识别任务中。

Addressing the issue of subpar performance in identifying shallow water marine life in underwater environments using existing methods,we propose an improved method based on the RT-DETR benchmark model.Initially,the reparameterization network RepViT is utilized as the backbone of the model,enhancing its feature extraction capabilities.Subsequently,a reparameterized parallel dilated convolution(RepPDC)is constructed and incorporated into the neck network,enabling the model to effectively capture long-range contextual information,thereby improving the model′s recognition accuracy.Lastly,a bidirectional feature fusion module(CAFM)is constructed based on the attention mechanism,enhancing the model′s ability to focus on key information in underwater environments.Experimental results demonstrate that the improved method significantly boosts the mAP50 to 87.5%,mAP75 to 70.9%,and mAP50:95 to 64.9%,with fewer parameters,making it a promising candidate for practical applications in the identification of shallow water marine life.