基于改进实例分割算法的智能猪只盘点系统设计

基于图像处理的动物资产计数方法，不仅可以减少人工投入，还可以缩短生物资产的计数周期，但该方法受光照条件影响严重，并且当动物间相互挤压、遮挡时，计数精度较差。针对这些问题，该研究提出了一种基于图像实例分割算法的生猪计数网络。针对光照和目标边缘模糊问题，利用拉普拉斯算子进行图像预处理。对Mask R-CNN网络的特征提取网络进行改进，在原始特征金字塔网络（Feature pyramid network, FPN）后面增加一条自底向上的增强路径，直接将低层边缘位置特征与高层特征相融合，提高对目标边缘轮廓的识别能力，对非极大值抑制过程和损失函数进行优化和改进，以提高分割精度。在河北丰宁、吉林金源和内蒙古正大3个试验猪场进行测试，验证本文网络的计数精度。采集设备在3个试验猪场共采集2 400张图像，经图像预处理去除模糊和光线差的图像，从剩余的图像中随机选取共1 250张图像作为原始数据集，其中丰宁猪场500张、金源猪场500张，正大猪场250张。将各猪场的原始数据集分别按2:2:1的比例分为3部分，包括训练集905张，验证集95张，测试集250张，对原始训练集和验证集进行数据增强，最终得到训练集图像1 500张，验证集图像150张，测试集图想250张。河北和吉林的试验猪场，每栏猪只数目为12～22头，各测试100张图像，完全准确清点的图像比例分别为98%和99%，满足实际应用要求。内蒙古试验猪场的单栏猪只密度大，每栏猪只数目平均80头，测试50张图像，完全准确清点的图像比例为86%。本文所提出的猪只盘点系统，通过修改网络增强图像中目标特征信息提取和优化边界框回归过程，减少由于光线差和遮挡导致的目标漏检情况，解决了基于图像分割算法的猪只盘点中光照、模糊以及遮挡等问题，能够满足单栏饲养密度为1.03～1.32头/m2的养殖场的猪只盘点需求。

Design of intelligent pig counting system based on improved instance segmentation algorithm

The existing pig counting system based on image processing are seriously affected by light conditions, and the counting accuracy is poor when pigs are crowded and obscured.To realize the intelligent pig counting, the paper proposed a counting scheme based on the improved instance segmentation algorithm was proposed in this paper. Aiming at the problems of image illumination and target edge blur, Laplace operator was used to preprocess the images. The feature extraction network of MASK R-CNN network was improved by using Resnet-152 as the Mask R-CNN feature extraction network, and the original Feature Pyramid Network(FPN) was followed by a bottom-up enhancement path, which directly fused the low-level edge features with the high-level features to improve the recognition ability of the target edge bluring. The non maximum suppression process and loss function were optimized and improved to improve the segmentation accuracy. The experiments were carried out at three different real pig farms to verify the counting accuracy, respectively in Hebei province, Jilin province and Inner Mongolia. The size of the cage in Hebei pig farm was 5.5 m × 1.8 m, with an average of 12 pigs in a single pen, with a feeding density of 1.21 pigs/m2; the size of the cage in Jilin pig farm was 5.5 m × 3.9 m, with an average of 22 pigs in a single pen, and the average rearing density was 1.03 pigs/m2; the size of the cage in Inner Mongolia pig farm was 11.4 m × 5.28 m, with an average of 80 pigs in a single pen, the average feeding density was 1.32 pigs/m2. The RGB camera is wa nstalled on the top of the pen and acquired the image in daytime. 2 400 images were collected in total, and 2000 images were selected after image preprocessing, and 1 250 images of three pig farms were selected as the original data set according to the ratio of 2:2:1. The training set and verification set were enhanced to 1 500 and 150 images and 250 images for the test set, The experimental results showed that 98 images could realize exact counting and 2 images missed 1 pig in the Jilin pig farm, the accuracy of pig counting was 98%. In Hebei pig farm, 99 images could realize the exact counting and the accuracy of pig counting was 99%. For Inner Mongolia pig farms with high feeding density, the accuracy of pig counting was 86%, among the 50 test images, 7 images missed detection, 4 images missed 1 target, 3 images missed 2 targets, The results can provide the application of the artificial intelligent in agriculture field.