基于多尺度融合网络的轻量化牛脸识别算法

Lightweight Cattle Face Recognition Algorithm Based on Multi-scale Fusion Network

摘要:

在牛场智能化养殖中，牛只面部识别技术的运用至关重要。然而，由于牛场养殖环境复杂多变以及牛脸形态的非平面特性，使得面部识别技术在实际应用中常面临识别精度不足、鲁棒性较差等问题。针对上述问题，提出一种基于FaceNet的多尺度融合网络(FaceNet based on multi-scale fusion, FMF)，通过牛只面部特征识别牛只身份。采用MSRCR方法对输入牛脸图像进行色彩恢复预处理，降低光照对FMF算法的影响；在主干特征提取网络中引入更加轻量化的MobileNetV3，在保证较高特征提取能力的前提下减小模型参数量和计算量；提出一种对于特征多尺度融合的注意力机制M_CBAM(Composite dual-branch adaptive attention)，M_CBAM可依据特征图重要特征调整加权系数，自适应加权串行CBAM与并行CBAM的权重，然后进行牛脸局部细微特征和全局特征的多尺度特征融合，提升牛脸识别精度。为探索本文算法的有效性和实时性，在自制牛脸数据集上进行消融实验，并将所得结果与当前主流的识别算法进行对比，最后将其部署至Jetson AGX Xavier嵌入式平台上进行算法应用测试。结果表明，本文算法在其他奶牛场采集的开集测试集上准确率达到93.86%，帧率达到30.02f/s，在模型推理速度较快的条件下，识别精度明显优于原网络与对比网络。

Abstract:

In intelligent cattle farming, the application of facial recognition technology for cattle is crucial. However, due to the complex and variable farming environment and the non-planar characteristics of cattle faces, facial recognition technology often faces challenges such as insufficient recognition accuracy and poor robustness in practical applications. To address these issues, a FaceNet based on multi-scale fusion network (FMF) was proposed, which identified cattle through facial features. Firstly, the MSRCR method was used for color restoration preprocessing of the input cattle face images to reduce the impact of lighting on the FMF algorithm. Subsequently, a more lightweight MobileNetV3 was introduced into the main feature extraction network to reduce the model parameters and computation while ensuring high feature extraction capability. Finally, a composite dual-branch adaptive attention mechanism (M_CBAM) was proposed for multi-scale feature fusion. M_CBAM adjusted the weighting coefficients based on important features of the feature maps, adaptively weighting serial CBAM and parallel CBAM weights, then performed multi-scale feature fusion of local fine features and global features of cattle faces, improving facial recognition accuracy. To explore the effectiveness and real-time performance of the proposed algorithm, ablation experiments were conducted on a self-made cattle face dataset, and the results were compared with current mainstream recognition algorithms. Finally, the algorithm was deployed on the Jetson AGX Xavier embedded platform for application testing. The test results showed that the proposed algorithm achieved an accuracy of 93.86% and an FPS of 30.02 f/s on an open test set collected from other dairy farms. Under the condition of fast model inference speed, the recognition accuracy was significantly better than that of the original network and comparison networks.