针对水下目标检测中存在的图像模糊、小目标众多导致YOLOv8n目标检测算法的漏检、误检问题,提出一种改进YOLOv8n的轻量级水下目标检测算法.首先,在主干网络部分采用空间到深度层的非跨步卷积替代部分卷积,并引入全局注意力机制,以增强全局上下文信息,从而提升主干网络对模糊目标和小目标特征的提取能力.其次,使用轻量级上采样算子CARAFE(content aware reassembly of features)替换原有的上采样方法,以扩大模型的感受野.然后,引入归一化高斯瓦萨斯坦距离,并将其与完全交并比结合,构建一种新的定位回归损失函数,以提高模型在复杂水下环境下对小目标的定位精度.最后,提出结合参数化修正线性单元(PReLU)的动态目标检测头(PR-DyHead),以优化原有的检测头性能,增强模型对水下小目标的处理能力.实验结果表明,改进的YOLOv8n算法在RUOD数据集上的平均精度均值为 86.62%,比原始YOLOv8n算法提升了 3.20百分点,模型参数量为 5.67 M,千兆浮点计算量为 12.5,满足轻量级要求.
Lightweight Underwater Target Detection Algorithm Based on Improved YOLOv8n
In response to the challenges of fuzzy image and numerous small targets in underwater target detection,which lead to missed detection and false detection with the YOLOv8n algorithm,we proposed an enhanced lightweight underwater target detection algorithm.Initially,within the backbone network,certain convolutions were substituted with non-strided space-to-depth convolution,and a global attention mechanism was introduced to augment global contextual information,thereby improving the network's ability to extract features from blurry and small targets.Subsequently,the conventional upsampling method was replaced with a lightweight upsampling operator,content aware reassembly of features,to broaden the model's receptive field.Furthermore,the normalized Wasserstein distance was introduced and integrated with complete intersection over union to devise a novel localization regression loss function,aimed at increasing the accuracy of small target localization in complex underwater environment.Finally,a dynamic target detection head combined with parameterized rectified linear unit was proposed to enhance the performance of the original detection head,thereby improving the model's proficiency in managing small underwater targets.Experimental results demonstrated that the improved YOLOv8n algorithm achieved a mean average precision of 86.62%on the RUOD dataset,marking a 3.20 percentage points improvement over that of the original YOLOv8n algorithm.The total number of model parameters was 5.67 M,with the number of gigabit floating-point operations is 12.5,fulfilling the criteria for lightweight model.
deep learningunderwater target detectionYOLOv8nlightweight algorithmglobal attention mechanism
NETL
NSTL
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