A muzzle-based self-enhancing cow recognition method using YOLO and LLM

Abstract

In this research, we have presented an approach to muzzle-based cow identifica tion. To recognise muzzles, we have used you only look once (YOLO) and the structural similarity index (SSI). In the proposed method, a large amount of data is not required for the image processing task. Because here we included a self enhancing feature, which helps the model to predict accurately and also helps to improve performance by increasing the amount of data. This certain program helps the model to improve its capability for delivering faster responses. large language model (LLM) is responsible for adding the self-enhancing feature. On the other hand, to find out the most suitable YOLO model for our research, we have trained data with three different versions of YOLO: YOLO v7, v8, and v9. We evaluated the performances of these three versions in terms of Preci sion, Recall, mAP50, and mAP50-95 and found YOLO v9 to be the best among them. It excels with a precision of 0.391, a recall of 0.916, a mAP50 of 0.559, and a mAP50-95 of 0.307. Here, data have been collected manually and were annotated by Roboflow.