Detection and Counting Method of Pigs Based on YOLOV5_Plus: A Combination of YOLOV5 and Attention Mechanism

Abstract

Information-based pig detection and counting is the trend in smart animal husbandry development. Cameras can efficiently collect farm information and combine it with artificial intelligence technology to assist breeders in real-time monitoring and analysis of farming. In order to improve the speed and accuracy of pig detection and counting, an advanced improved YOLO_v5 method for pig detection and counting based on the attention mechanism is proposed. The model is named as YOLOV5_Plus. This article utilizes a series of data augmentation methods, including translation, color augmentation, rescaling, and mosaic. The proposed model performs feature extraction on the original image with a backbone network, detects pigs of different sizes with three detection heads, and counts the detected anchor frames. Different versions of YOLOV5 are compared, and YOLOV5x is selected as the baseline model for the best performance. Attention modules are smartly combined with the model so that the model can better handle overlapping and misidentification. YOLOV5_Plus can achieve an accuracy of 0.989, a recall of 0.996, mAP@.50 of 0.994, and mAP@.50:.95 of 0.796, which outperforms all competing models. The inference time per image during detection is only 24.1 ms. YOLOV5_Plus model achieves real-time pig number and location detection, which is meaningful for promoting smart animal husbandry and saving labor costs in farming enterprises.

Visual Detection of Lost Ear Tags in Breeding Pigs in a Production Environment Using the Enhanced Cascade Mask R-CNN

“…The AP at each IoU threshold is computed as the area under the precision-recall curve (IoU curve) bounded by the x-axis and the curve itself. Precision and recall are calculated using Equations ( 11) and (12), respectively; the mAP is then calculated using Equation (13), and the F1 score is calculated using Equation (14).…”

“…A DCNN offers several advantages in breeding pig production, including high recognition rates, non-invasiveness, minimal animal stress response, and easy deployment. It enables real-time, efficient, and continuous detection, making it suitable for tasks such as individual recognition [4][5][6], pose detection [7][8][9], target tracking [10,11], and count statistics [12,13]. Previous studies have primarily focused on learning the image feature representation of breeding pigs, extracting features, and using image-based classification and object recognition for practical applications.…”

iblueCulture: Data Streaming and Object Detection in a Real-Time Video Streaming Underwater System

“…The confusion matrix is a table that shows how many correct and how many wrong predictions a model has made. TP stands for true positive (there is an object in the image and the algorithm makes a correct prediction), FP stands for false positive (there is no object in the image, but the algorithm detects one) and FN stands for false negative (the algorithm fails to locate the object in the image) [37].…”

YOLOv5-SA-FC: A Novel Pig Detection and Counting Method Based on Shuffle Attention and Focal Complete Intersection over Union

“…We also found some existing research used in the detection of pigs with YOLOv5 (e.g., Lai [38], Li [39], and Zhou [40]) and compared them with our method. The specific results are shown in Table 6 and were assessed using the mAP@0.5 metric: From Table 6, it can be seen that our YOLOv5-SA performs the best.…”