基于上下文多尺度融合的棉铃计数算法

由于实际的棉田环境中存在高度遮挡及尺度多变问题,大幅降低了目标计数算法的精度.针对这一问题,提出基于上下文多尺度融合的棉铃计数算法.算法由金字塔结构的上下文模块和融合卷积神经网络两个部分组成.首先通过全局上下文和局部上下文模块对棉铃图像的上下文信息编码,同时利用多列特征转换模块将输入图像映射成高维特征,最后通过融合卷积神经网络将上下文信息与高维特征进行融合,实现高精度棉铃计数并生成高质量棉铃密度图.此外,从近距离和地空观测两个角度在棉铃数据集上进行实验,实验结果表明,引入上下文信息可以有效提升棉铃计数精度,计数误差MAE和MSE分别下降了27.3和29.4.

Cotton bolls counting algorithm in field based on density level classification

The severe occlusion and scale variation in the cotton field reduces the accuracy of the object counting algorithm greatly. To solve above problems, this paper presented a cotton boll counting algorithm based on context multi-scale fusion. This algorithm consisted of the context module of the pyramid structure and the fusion convolutional neural network. Firstly, it adopted global context and local context modules encode the context information in the cotton images, and then used multi-co-lumn feature conversion module to map the input image into high-dimensional feature. Finally, it combined the context information with the high-dimensional feature through the fusion convolutional neural network, for achieving high-precision cotton boll counting and generating high-quality cotton boll density map. In addition, this paper conducted experiments on the cotton boll dataset from two angles of close-range and ground-air observation. The comparative experimental results show that adopting context information can effectively improve the accuracy of cotton boll counting, and the counting errors MAE and MSE have decreased by 27.3 and 29.4, respectively.