Abstract
Instance segmentation is an accurate and reliable method to segment adhesive pigs’ images, and is critical for providing health and welfare information on individual pigs, such as body condition score, live weight, and activity behaviors in group-housed pig environments. In this paper, a PigMS R-CNN framework based on mask scoring R-CNN (MS R-CNN) is explored to segment adhesive pig areas in group-pig images, to separate the identification and location of group-housed pigs. The PigMS R-CNN consists of three processes. First, a residual network of 101-layers, combined with the feature pyramid network (FPN), is used as a feature extraction network to obtain feature maps for input images. Then, according to these feature maps, the region candidate network generates the regions of interest (RoIs). Finally, for each RoI, we can obtain the location, classification, and segmentation results of detected pigs through the regression and category, and mask three branches from the PigMS R-CNN head network. To avoid target pigs being missed and error detections in overlapping or stuck areas of group-housed pigs, the PigMS R-CNN framework uses soft non-maximum suppression (soft-NMS) by replacing the traditional NMS to conduct post-processing selected operation of pigs. The MS R-CNN framework with traditional NMS obtains results with an F1 of 0.9228. By setting the soft-NMS threshold to 0.7 on PigMS R-CNN, detection of the target pigs achieves an F1 of 0.9374. The work explores a new instance segmentation method for adhesive group-housed pig images, which provides valuable exploration for vision-based, real-time automatic pig monitoring and welfare evaluation.
Highlights
With the development of artificial intelligence and automation technology, utilizing video cameras to monitor the health and welfare of pigs has become more important in the modern pig industry
In group-housed environments, instance segmentation of pigs includes detection, which automatically obtains the positions of all pigs, and segmentation, which distinguishes each pig in the images [1]
Detecting and segmenting group-housed pigs can help improve the efficiency of instance segmentation, complete high-level applications, and improve the welfare of pigs in pig farms
Summary
With the development of artificial intelligence and automation technology, utilizing video cameras to monitor the health and welfare of pigs has become more important in the modern pig industry. In group-housed environments, instance segmentation of pigs includes detection, which automatically obtains the positions of all pigs, and segmentation, which distinguishes each pig in the images [1]. Pig detection and segmentation methods include two categories based on non-deep learning and deep learning algorithms These non-deep learning approaches have more mature technologies and have been widely applied in video surveillance of pigs [1,10,11,12]. Guo et al [10] proposed a pig’s foreground detection method based on the combination of a mixture of Gaussians and threshold segmentation. This approach achieved an average pig object detection rate with approximately 92% in complex scenes. Li et al [1]
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