E-YOLO: Recognition of estrus cow based on improved YOLOv8n model

Abstract

Timely and accurately identifying estrus cows is essential in modern dairy farming. To address the challenges of delayed and inefficient manual monitoring of cow estrus, an improved model based on You Only Look Once v8 Nano (YOLOv8n), named Estrus-YOLO (E-YOLO), was proposed to identify estrus cows efficiently. In this research, the dataset was labelled not only for cow mounting behavior but also innovatively labelled individual estrus cows, enabling precise identification of estrus cows. Due to the small size of cow in the field of view, the Complete Intersection over Union (CIoU) loss was replaced with the Normalized Wasserstein Distance (NWD) loss to reduce sensitivity to position deviations of target cows. Context Information Augmentation Module (CIAM) was proposed to enhance the contextual information for estrus cows by utilizing cow mounting behavior as reference features. Furthermore, the Triplet Attention Module (TAM) was incorporated into the Backbone to enhance the network’s focus on individual estrus cows through cross-dimensional interactions. To validate the effectiveness of the algorithm, experiments were conducted on a dataset consisting of 1716 instances of cow mounting behavior. The experimental results demonstrated that the proposed model achieved an Average Precision of estrus (APestrus) of 93.90%, Average Precision of mounting (APmounting) of 95.70%, F1-score of 93.74%, detection speed of 8.1 ms/frame, with the parameters of 3.04 M, and the Floating-point Operations (FLOPs) of 9.9 G. Compared to the YOLOv8 model, the proposed model exhibited an improvement of 5.40% in APestrus and 3.30% in APmounting. When compared to Single Shot MultiBox Detector (SSD), Faster Region Convolutional Neural Network (Faster R-CNN), YOLOv5n, YOLOv5s, YOLOv7-tiny, YOLOv8n, and YOLOv8s, the proposed model had fewer parameters, FLOPs, and fast detection speed. Except for APmounting, which was slightly lower than SSD, the rest accuracy indexes were the highest, showing good comprehensive performance and meeting the requirements of accurate and rapid identification of estrus cows. The proposed model was helpful for accurate and real-time monitoring of estrus cows in complex breeding environments and all-weather conditions.