Comparison of cow face target detection algorithms based on deep learning

Abstract

To address issues in bull face detection, such as unsatisfactory inspection results and fragile inspection devices, we conducted a comparative study on existing representative deep network models (e.g. Mask R-CNN, YOLO, SSD, etc.) based on big data analysis discrepancy theory. The experimental results show that Mask R-CNN has the most efficient comprehensive analysis, although its detection rate is relatively slow. YOLO has a higher detection rate but less efficient comprehensive analysis compared to Mask R-CNN. SSD has the lowest comprehensive efficiency among the models.