基于改进的轻量版SOLOv2红鳍东方鲀实例分割方法

Instance segmentation method of Takifugu rubripes based on improved light SOLOv2

为解决红鳍东方鲀养殖密度不均导致图像分割精度低和小目标分割效果差的问题,提出一种改进的轻量版SOLOv2实例分割方法。首先进行可变形卷积(deformable convolutional networks,DCN)网络结构的优化调整,通过在卷积核上增加偏移参数,调整卷积的感受野,使感受野与物体的实际形状更加贴近;再在残差模块最后一层引入无参数注意力机制SimAM,捕捉图像中更多的局部信息,获得不同尺度的目标特征,优化模型对小目标分割的性能。试验结果显示,改进后的轻量版SOLOv2模型较原有模型平均分割精度提高了3.7个百分点,对小目标的分割精度提升了1.4个百分点,同时加入DCN和SimAM注意力模块后,模型的分割精度提高到65.2%。结果表明,改进后的SOLOv2模型可以提高边界处的细节感知能力,强化模型对小目标鱼群特征的提取能力,可用于高密度场景下的精准实例分割,实现红鳍东方鲀鱼群目标精准像素级分割。

In order to solve the problems of low image segmentation accuracy and poor segmentation results for small targets caused by the uneven density of Takifugu rubripes,an instance segmentation method based on improved light SOLOv2 is proposed.Firstly,the structure of deformable convolutional networks(DCN) is optimized by adjusting the receptive field of the convolution using offset parameters.This adjustment enables the receptive field to be closer to the actual shape of the object,leading to better segmentation accuracy.Next,the parameter-free attention mechanism SimAM is fused in the last layer of the residual module to capture more local information in the image,obtain target features at different scales,and optimize the performance of the model for small target segmentation.The experimental results show that the average segmentation accuracy of the improved lightweight SOLOv2 model was improved by 3.7percentage,and the segmentation accuracy of small targets was improved by 1.4 percentage compared with the original model.After adding both DCN and SimAM attention modules,the segmentation accuracy of the model increased to 65.2%.The results show that the improved SOLOv2 model can improve the detail perception at the boundary,strengthen the model's ability to extract the features of small target fish stocks,and can be used for accurate instance segmentation in high-density scenarios to achive accurate pixel-level segmentation of Takifugu rubripes.