基于密度等级分类的田间棉铃计数算法

Cotton Bolls Counting Algorithm in the Field Based on Density Level Classification

为了解决复杂棉田环境中的多模态棉铃计数问题,提出一种基于密度等级分类的田间棉铃计数算法.首先采用密度等级分类估计器对图像中的全局上下文信息进行编码;然后利用多列结构的密度图估计器将输入图像转换为高维特征映射;最后通过特征融合神经网络,将分类信息与高维特征映射相结合,以生成高质量的密度图,进而实现对田间棉铃进行计数.此外,构建了一个包含412幅田间棉铃图像的数据集,该数据集可根据不同的环境、年份和地域条件进行划分,以进行实验和对比.实验结果表明,所提出的算法达到了更低的计数误差,其有效性和鲁棒性均优于其他对比算法.

To solve the problem of multi-mode cotton boll counting in the complicated environment,an in-field cotton boll counting algorithm based on density classification is proposed.Firstly,the algorithm encodes the global context information with a density level classification estimator.Then the input images are converted into high-dimensional feature maps by density map estimator with multi-column structure.Finally,through the feature fusion neural network,the classification information is combined with high-dimensional feature maps to generate high-quality density map,and then the cotton bolls are counted.In addition,a new dataset within 412 in-field cotton boll images is constructed for experiment and comparison,which can be divided by different environment,year and region conditions.Experimental results demonstrate that the proposed algorithm achieves a lower counting error,and better effectiveness and robustness than other comparison algorithms.