CSP-YOWO-TrajNet: A spatio-temporal detection method for laying hen heat stress behavior

Mental and physical well-being is a prime factor that allows laying hens to exhibit natural behaviour. It is difficult to monitor the heat stress behavioral changes in laying hens for their welfare and production efficiency. This study proposed the real-time detection of those changes. However, three problems arise in battery-cage systems: birds occlude each other, behaviour labels are coarse, and edge devices have limited resources. To solve these problems, we proposed a three-dimensional spatiotemporal action detector named CSP-YOWO-TrajNet. A 2-D CNN and a 3-D CNN were designed to track and perceive trajectories to address these issues. Initially, CSPDarknet53-SPA was inserted into the 2-D branch to strengthen spatial feature extraction. Second, 3-D ResNeXt-50 was adopted in the 3-D branch for efficient temporal modelling. Finally, a TrajNet module was added to predict trajectories and improve perception and tracking. A video data set of 281 hen-behaviour clips was built for evaluation. The proposed CSP-YOWO-TrajNet achieved a precision of 94.1 % and an mAP@50 of 96.1 %. Compared with the YOWO baseline, precision and mAP@50 were raised by 3.0 % and 3.6 %, respectively, and the model size was reduced from 134.0 MB to 78.8 MB. In terms of tracking, the F1 score reached 95.4 % and the tracking accuracy reached 89.2 %. The proposed detector therefore supports real-time recognition of heat-stress behaviours in laying hens and can be deployed on edge devices.