Liang Jinyu, Cai Weiwei, Xu Zhuonong, Zhou Guoxiong, Li Johnny, Xiang Zuofu
College of Computer & Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China.
School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China.
Animals (Basel). 2023 May 17;13(10):1660. doi: 10.3390/ani13101660.
In a natural environment, factors such as weathering and sun exposure will degrade the characteristics of dog feces; disturbances such as decaying wood and dirt are likely to make false detections; the recognition distinctions between different kinds of feces are slight. To address these issues, this paper proposes a fine-grained image classification approach for dog feces using MC-SCMNet under complex backgrounds. First, a multi-scale attention down-sampling module (MADM) is proposed. It carefully retrieves tiny feces feature information. Second, a coordinate location attention mechanism (CLAM) is proposed. It inhibits the entry of disturbance information into the network's feature layer. Then, an SCM-Block containing MADM and CLAM is proposed. We utilized the block to construct a new backbone network to increase the efficiency of fecal feature fusion in dogs. Throughout the network, we decrease the number of parameters using depthwise separable convolution (DSC). In conclusion, MC-SCMNet outperforms all other models in terms of accuracy. On our self-built DFML dataset, it achieves an average identification accuracy of 88.27% and an F1 value of 88.91%. The results of the experiments demonstrate that it is more appropriate for dog fecal identification and maintains stable results even in complex backgrounds, which may be applied to dog gastrointestinal health checks.
在自然环境中，风化和阳光照射等因素会使狗粪便的特征退化；腐烂的木材和污垢等干扰因素很可能导致误检测；不同种类粪便之间的识别差异很小。为了解决这些问题，本文提出了一种在复杂背景下使用MC-SCMNet对狗粪便进行细粒度图像分类的方法。首先，提出了一种多尺度注意力下采样模块（MADM）。它仔细检索微小粪便特征信息。其次，提出了一种坐标位置注意力机制（CLAM）。它抑制干扰信息进入网络的特征层。然后，提出了一个包含MADM和CLAM的SCM模块。我们利用该模块构建了一个新的骨干网络，以提高狗粪便特征融合的效率。在整个网络中，我们使用深度可分离卷积（DSC）减少参数数量。总之，MC-SCMNet在准确率方面优于所有其他模型。在我们自建的DFML数据集上，它实现了88.27%的平均识别准确率和88.91%的F1值。实验结果表明，它更适合狗粪便识别，即使在复杂背景下也能保持稳定结果，可应用于狗的胃肠道健康检查。
