Detection of abnormal chicken droppings based on improved Faster R-CNN

Abstract

The characteristics of chicken droppings are closely related to the health of chickens. Veterinarians often judge the health of a chicken by looking at whether the chicken poop is normal. At present, the inspection of abnormal chicken droppings in chicken coops relies on manual observation, which is inefficient, accurate varies from person to person, labor-intensive, and has the risk of cross-infection. To achieve efficient, accurate, and intelligent identification of abnormal chicken droppings, an abnormal chicken droppings detection method based on improved Faster Region-based Convolutional Neural Network (Faster R-CNN) was proposed in this study. In the feature extraction network stage, deformable convolution was used and combined with Path Augmentation-Feature Pyramid Network (PA-FPN) to improve the extraction ability of features at different scales. In the Region Proposal Network (RPN) stage, the K-means++ algorithm was used to cluster the dataset and obtain the Anchor-ratio which is more suitable for the chicken poop object, and the FocalLoss classification loss function was used to improve the classification ability of difficult samples. In the regional convolutional network stage, the region of interest calibration algorithm was used instead to obtain more accurate localization information. The experimental results show that the improved Faster R-CNN structure can reach an accuracy of 98.8% for abnormal chicken poop detection, and the average accuracy mean value was improved by 27.8%. The results can provide a key core technology support for establishing an efficient abnormal chicken droppings online detection system. Keywords: abnormal chicken droppings, Faster R-CNN, detection, non-destructive monitoring, PA-FPN DOI: 10.25165/j.ijabe.20231601.7732 Citation: Zhu J H, Cui Z H, Wang H Y, Zhou M, Sun X Q. Detection of abnormal chicken droppings based on improved Faster R-CNN. Int J Agric & Biol Eng, 2023; 16(1): 243–249.