Abstract
Individual identification plays an important part in disease prevention and control, traceability of meat products, and improvement of agricultural false insurance claims. Automatic and accurate detection of cattle face is prior to individual identification and facial expression recognition based on image analysis technology. This paper evaluated the possibility of the cutting-edge object detection algorithm, RetinaNet, performing multi-view cattle face detection in housing farms with fluctuating illumination, overlapping, and occlusion. Seven different pretrained CNN models (ResNet 50, ResNet 101, ResNet 152, VGG 16, VGG 19, Densenet 121 and Densenet 169) were fine-tuned by transfer learning and re-trained on the dataset in the paper. Experimental results showed that RetinaNet incorporating the ResNet 50 was superior in accuracy and speed through performance evaluation, which yielded an average precision score of 99.8% and an average processing time of 0.0438 s per image. Compared with the typical competing algorithms, the proposed method was preferable for cattle face detection, especially in particularly challenging scenarios. This research work demonstrated the potential of artificial intelligence towards the incorporation of computer vision systems for individual identification and other animal welfare improvements.
Highlights
Animal husbandry is undergoing a transition from extensive farming to precision livestock farming and welfare breeding
Advancements in deep learning networks present an opportunity to extend the research to the empirical comparisons of the typical Convolutional Neural Network (CNN) backbones for RetinaNet in the task of detecting multi-view cattle face
ThFePresFuNlts indicate that RetinaNet is most competent in real-world practice as the datasets are in docifcfleRuresetinionFtnaa.NscYtYoeeootrmll+Roopv-vRCl3e3eNxsaNNnsedct e5Fn0aessterwR000i...t-999hC989N658s870Nevae000cr...110eh354i623ef868vaceed-pn000oe...as899er799l048y000vsairmiaitlia111ornpearn000fo...d999r399m079d000aifnfceer455ew900n800itthdReeg7312trieneasN200oeft in AP (99.68% for Yolov3 and 99.8% for RetinaNet) and F1 score
Summary
Animal husbandry is undergoing a transition from extensive farming to precision livestock farming and welfare breeding. Considering the practical scenario of multi-face detection task of livestock cattle identity authentication, Gou et al improved Faster R-CNN by substituting ZF network for Inception v2 as the basic network [36]. Given the urgent need to develop technologies that can assist with livestock production and welfare management, it is timely to assess the application of a state-ofthe-art machine learning algorithm for precision livestock monitoring. Due to their great significance concerning animal husbandry, cattle were chosen as the case study to explore the performance of RetinaNet-based object detection for multi-view face detection
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