Night-DETR:基于夜间环境下肉牛行为识别方法OA

Behavioral information of beef cattle has been widely acknowledged as the critical indicator related to physiological health status,welfare,and production.Therefore,it is often required to accurately recognize cattle behaviors for health evaluation and early abnormality warning in precision livestock farming.However,visual monitoring has been severely restricted under nighttime breeding environments due to the low-illumination imaging.Insufficient lighting,uneven illumination distribution,and motion-induced blur have significantly degraded image quality,thus resulting in blurred appearances and weak behavioral feature representations.Existing machine vision can also suffer from frequent missed detections and false positions during feature extraction and classification.In this study,a behavior recognition framework,named Night-DETR,was proposed to specifically monitor beef cattle under complex nighttime scenarios.First,a low-light image enhancement with SCINet was introduced as a preprocessing module to improve the visual quality of nighttime monitoring images.Image brightness and structural clarity were effectively increased by suppressing noise after enhancement,thereby providing more informative and stable visual inputs for downstream behavior recognition.Second,the baseline RT-DETR architecture was systematically redesigned to improve the detection accuracy and computational efficiency.Particularly,StarNet was adopted as the backbone network to replace conventional heavy feature extractors.Multi-scale features were efficiently represented to reduce parameter redundancy and computation.An Adaptive Interactive Feature Integration module with an Efficient Discriminative Frequency Domain-based FFN(AIFI-EDFFN)was designed to treat feature ambiguity and inter-object interference under low-light conditions.Behavioral features were enhanced to suppress irrelevant background responses to contextual interactions among neighboring cattle,thereby strengthening contour,posture,and spatial relationship representations.In addition,an Intensity Enhance Layer Cross-scale Feature Fusion module(IELC3)was constructed to optimize multi-scale feature aggregation.An illumination enhancement layer was embedded into the cross-scale fusion.Limb structures,body orientation,and posture features were typically obscured to emphasize them in nighttime environments,thus improving the sensitivity to subtle behavioral changes under weak lighting.A series of experiments was conducted to evaluate the Night-DETR model.The performance of Night-DETR was compared with the representative and state-of-the-art object detection frameworks,including Faster R-CNN,TOOD,FCOS,YOLOv11n,YOLOv12n,YOLOv13n,and the original RT-DETR.Experimental results demonstrated that Night-DETR consistently performed best in nighttime detection scenarios.Compared with the baseline RT-DETR model,Night-DETR improved precision,recall,and mean average precision by 5.3,5.8,and 5.1 percentage points,respectively,thus achieving 93.4％precision,90.8％recall,and 91.3％mAP@0.5.Moreover,the computational complexity was significantly reduced,with the number of parameters and floating-point operations decreased by 55.8％and 58.2％,respectively.A lightweight architecture was obtained with only 8.8 M and 23.8 G.Cross-scene transfer learning experiments were conducted to verify the generalization of the improved model among different farming scenarios.An average precision of 90.8％was achieved after transfer learning,which was 1.4 percentage points higher than the original.Night-DETR shared strong cross-scene generalization in various farming environments.Additionally,the robustness of the model was evaluated under extremely low-light conditions.The performance curves were plotted for precision,recall,and mAP@50 among different illumination gradients.Night-DETR maintained stable performance even in severely dark scenarios,indicating its strong robustness under extremely low-light environments.Overall,the Night-DETR model can be expected to provide accurate behavioral perception under low-illumination environments with the lightweight deployment in the intelligent nighttime health monitoring and abnormal behavior early-warning systems.The findings can also offer strong technical support for precision livestock farming in breeding environments.