Group-housed pigs and their body parts detection with Cascade Faster R-CNN

Abstract

The detection of individual pigs and their parts is a key step to realizing automatic recognition of group-housed pigs’ behavior by video monitoring. However, it is still difficult to accurately locate each individual pig and their body parts from video images of groups-housed pigs. To solve this problem, a Cascade Faster R-CNN Pig Detector (C-FRPD) was designed to detect the individual pigs and different parts of their body. Firstly, the features were extracted by 101-layers Residual Networks (ResNet-101) from video images of group-housed pigs, and the features were input into the region proposal networks (RPN) to obtain the region proposals. Then classification and bounding box regression on region proposals were performed to get the location of each pig. Finally, the body parts of the pig were determined by using the Cascade structure to search on the feature map of the pig body area. These operations completed the detection of the whole body of each pig and its different parts of the body, and established the association between the whole and parts of the body in the end-to-end detection. In this study, 1500 pig pen images were trained and tested. The test results showed that the detection accuracy of C-FRPD reached 98.4%. Compared with the Faster R-CNN without cascade structure, the average detection accuracy was increased by 4.3 percentage points. The average detection time of a single image was 259 ms. The method in this study could accurately detect and correlate the individual pig with its head, back, and tail in the image. This method can provide a technical reference for recognizing the behavior of group-housed pigs. Keywords: group-housed pigs, body parts detection, Faster R-CNN, Cascade structure DOI: 10.25165/j.ijabe.20221503.6286 Citation: Xiao D Q, Lin S C, Liu Y F, Yang Q M, Wu H L. Group-housed pigs and their body parts detection with Cascade Faster R-CNN. Int J Agric & Biol Eng, 2022; 15(3): 203–209.