Zhao Hongke, Mao Rui, Li Mei, Li Bin, Wang Meili
College of Information Engineering, Northwest A&F University, Yangling 712100, China.
Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture, Yangling 712100, China.
Animals (Basel). 2023 Apr 13;13(8):1338. doi: 10.3390/ani13081338.
Sheep detection and segmentation will play a crucial role in promoting the implementation of precision livestock farming in the future. In sheep farms, the characteristics of sheep that have the tendency to congregate and irregular contours cause difficulties for computer vision tasks, such as individual identification, behavior recognition, and weight estimation of sheep. Sheep instance segmentation is one of the methods that can mitigate the difficulties associated with locating and extracting different individuals from the same category. To improve the accuracy of extracting individual sheep locations and contours in the case of multiple sheep overlap, this paper proposed two-stage sheep instance segmentation SheepInst based on the Mask R-CNN framework, more specifically, RefineMask. Firstly, an improved backbone network ConvNeXt-E was proposed to extract sheep features. Secondly, we improved the structure of the two-stage object detector Dynamic R-CNN to precisely locate highly overlapping sheep. Finally, we enhanced the segmentation network of RefineMask by adding spatial attention modules to accurately segment irregular contours of sheep. SheepInst achieves 89.1%, 91.3%, and 79.5% in box AP, mask AP, and boundary AP metric on the test set, respectively. The extensive experiments show that SheepInst is more suitable for sheep instance segmentation and has excellent performance.
绵羊检测与分割在推动未来精准畜牧业的实施中将发挥关键作用。在养羊场中，绵羊倾向于聚集的特性以及不规则的轮廓给计算机视觉任务带来了困难，比如绵羊的个体识别、行为识别和体重估计。绵羊实例分割是一种能够缓解在同一类别中定位和提取不同个体相关困难的方法。为了在多只绵羊重叠的情况下提高提取单个绵羊位置和轮廓的准确性，本文基于Mask R-CNN框架，更具体地说是RefineMask，提出了两阶段绵羊实例分割方法SheepInst。首先，提出了一种改进的骨干网络ConvNeXt-E来提取绵羊特征。其次，改进了两阶段目标检测器Dynamic R-CNN的结构，以精确地定位高度重叠的绵羊。最后，通过添加空间注意力模块增强了RefineMask的分割网络，以准确分割绵羊的不规则轮廓。SheepInst在测试集上的框AP、掩码AP和边界AP指标分别达到了89.1%、91.3%和79.5%。大量实验表明，SheepInst更适合绵羊实例分割，并且具有优异的性能。
