A Novel YOLOv6 Object Detector for Monitoring Piling Behavior of Cage-Free Laying Hens

Abstract

Piling behavior (PB) is a common issue that causes negative impacts on the health, welfare, and productivity of the flock in poultry houses (e.g., cage-free layer, breeder, and broiler). Birds pile on top of each other, and the weight of the birds can cause physical injuries, such as bruising or suffocation, and may even result in death. In addition, PB can cause stress and anxiety in the birds, leading to reduced immune function and increased susceptibility to disease. Therefore, piling has been reported as one of the most concerning production issues in cage-free layer houses. Several strategies (e.g., adequate space, environmental enrichments, and genetic selection) have been proposed to prevent or mitigate PB in laying hens, but less scientific information is available to control it so far. The current study aimed to develop and test the performance of a novel deep-learning model for detecting PB and evaluate its effectiveness in four CF laying hen facilities. To achieve this goal, the study utilized different versions of the YOLOv6 models (e.g., YOLOv6t, YOLOv6n, YOLOv6s, YOLOv6m, YOLOv6l, and YOLOv6l relu). The objectives of this study were to develop a reliable and efficient tool for detecting PB in commercial egg-laying facilities based on deep learning and test the performance of new models in research cage-free facilities. The study used a dataset comprising 9000 images (e.g., 6300 for training, 1800 for validation, and 900 for testing). The results show that the YOLOv6l relu-PB models perform exceptionally well with high average recall (70.6%), mAP@0.50 (98.9%), and mAP@0.50:0.95 (63.7%) compared to other models. In addition, detection performance increases when the camera is placed close to the PB areas. Thus, the newly developed YOLOv6l relu-PB model demonstrated superior performance in detecting PB in the given dataset compared to other tested models.