Abstract: Pigs' positional change in the group pens is a key indicator reflecting the behavioral expression and welfare of pigs. In this study, RGB (Red Green Blue) images were used as data sources, and an improved Faster R-CNN (Faster Region Convolutional Neural Networks) algorithm for pigs' position recognition in group pen was proposed. The algorithm introduced the time series into the candidate box region algorithm, designed a hybrid of Faster R-CNN and lightweight CNN network, and improved the recognition accuracy and recognition speed. The Residual Network (ResNet) was used as the feature extraction convolutional layer to deepen the network depth, thereby improving the feature extraction ability to improve the algorithm robustness. The area of the pigs in the pen was judged by the PNPoly algorithm. The experiment was carried out 24 h of continuous 98 d of video recording on 3 pens (pen 3, 4, and 12) in 2 breeding stages (growing and fattening). Pen 3 and 4 had a smaller area and the size was 4.10 m×3.14 m. Pen 12 had a larger area and the size was 5.16 m×3.14 m. Bite chains had been equipped on the slatted floor area in pen 4 and 12. The experiment randomly extracted 25 000 pictures from the video for algorithm research, and 20 000 images from it as the training set, 3 000 as the verification set, and 2 000 as the test set. Through testing, when the network depth was 128 layers, both recognition accuracy and detection time could be considered. The recognition speed was 0.064 s/frame and the recognition accuracy was 96.7%. The optimal number of shared convolutional layers and neighborhood range rate were 128 and 0.3, respectively. The algorithm was used to obtain the heat map, position distribution ratio, and diurnal rhythm changes of the position distribution of pigs of different pens and feeding days. It was found that the locations of the pigs in all pens were significantly affected by the type of floor. There was a clear dividing line between the solid floor and the slatted floor in the heat map of all pens on all feeding days. The size of the pen had a significant effect on the position distribution of pigs. At the end of the growing stage, the proportion of pigs on the solid floor area in pen 12 was significantly increased by 8.2% and 41.7% compared with pen 3 and 4, respectively (P<0.05). At the end of the fattening stage, the proportion of pigs on the solid floor area in pen 12 was significantly increased by 7.9% and 30.4% compared with pen 3 and 4, respectively (P<0.05). And the distribution ratio of pigs on the solid floor area decreased with the increase of feeding days. From the change of diurnal rhythm, the rest period, activity period, and feeding period of the pigs were determined quickly, the night sleep time was 19:00-6:00, the lunch break time was 10:00-13:00, and the feeding time was 8:00-9:00 and 14:00-15:00 which coincided with the time of the manager added to feed. The distribution of pigs in the feeding period was affected by the installation position of the feeder, and therefore, when analyzing the free distribution pattern of pigs' position in group pens, it was recommended to exclude them or analyze them separately. Installing pig bite chains in the slatted floor area could help pigs distinguish the activity area from the lying area. This method realized the rapid and accurate identification of the position within the pigpen in the growing and fattening group mode. The result could also help to enrich the evaluation indicators of the herd behavior in the research of pigs' breeding facilities and environment control.