Rethinking lightweight sheep face recognition via network latency-accuracy tradeoff

Abstract

Deep learning has greatly improved the performance of sheep face recognition, but existing recognition methods usually adopt deeper and wider networks to obtain better performance, resulting in heavy computational burden and slow inference speed. This paper proposes a very lightweight sheep face recognition network, referred to as VLFaceNet, which achieves state-of-the-art (SOTA) latency-accuracy tradeoff. The basic module of VLFaceNet is VL, which uses inexpensive linear operations to complement redundant features and reduces the model size and computational complexity through structural re-parameterization during inference, improving inference speed. VLDBlock is formed by concatenating VL and ECA channel attention to enhance the effectiveness of channel-level feature extraction. VLFaceNet is formed by stacking VL and VLDBlock. By fusing features of different scales of VLFaceNet, sheep faces of different scales can be recognized, improving the recognition performance of the model. To address the problem of high similarity and difficulty in distinguishing white sheep faces, this paper proposes a scaling feature enhancement method SFE, which changes the color distribution and texture of sheep face images, improving the distinguishability between sheep face images and thus the recognition performance of VLFaceNet. The recognition performance gains of multiple recognition models demonstrate the effectiveness of SFE. On a self-built dataset, VLFaceNet achieves the best latency-accuracy tradeoff with an inference latency of 2.58 ms and a recognition accuracy of 97.75 %. This research is expected to promote the application of deep learning-based recognition methods in livestock breeding.