Nighttime beef cattle behavior recognition method based on improved YOLOv11n under low-light image enhancement

Behavior recognition of beef cattle has been one of the most essential means in the intelligent breeding and health monitoring of cattle sheds. Its recognition often depended on the breeding environment, including the lighting conditions, stocking density, and ground conditions. However, the behavioral identification can be challenging due to the blurred boundary of individual cattle. The beef cattle, as a group animal, can usually tend to rest in groups of 3 to 5. The behavioral patterns and movement of the cattle can interfere with the accuracy of behavior recognition. Especially, the image visibility is significantly reduced during monitoring at night. In addition, the noise interference can further reduce the performance of the traditional behavior recognition under night breeding environments. Conventional recognition of beef cattle behavior can also be limited to the low image visibility, fuzzy and variable behavior, as well as the noise interference. In this study, a framework of recognition was proposed for the beef cattle at night using low-light image enhancement. Firstly, the Lighten Diffusion model was used to enhance the behavior image of beef cattle at night. The clarity and visibility of the image enhanced the target detection. Secondly, the YOLOv11n model was utilized to construct the C3k2-CAFormerCGLU module. Local important features of beef cattle were obtained using the gating mechanism. The noise and irrelevant information were suppressed to avoid misjudgment on the individual boundary of beef cattle. The beef cattle behaviors were effectively distinguished against the background noise or cattle overlapping area. The spatial-to-depth conversion convolution (SPDConv) was used to improve the original subsampling of the model. The spatial and depth information flow was optimized in the convolution operation. There was a more accurate feature expression of the beef cattle at a long distance. The recognition accuracy of the beef cattle behavior was improved from a far perspective. Finally, a Feature Focusing module was introduced into the neck Network. The feature focusing pyramid network (FFPN) was constructed to enhance the feature expression of the target region. The fine-grained feature was extracted under different receptive fields. At the same time, a cross-scale feature fusion was adopted to make the features with rich context information, in order to propagate effectively between different detection scales. Furthermore, the multi-scale target recognition was improved in complex environments. The experimental results showed that the image enhancement was achieved in the four unsupervised low-light enhancement algorithms, including Zero-DCE, Zero-DCE++, Enlighten GAN, and Lighten Diffusion, compared with the low-light image enhancement dataset. The Lighten Diffusion algorithm performed best in the low-light image enhancement tasks, with the highest peak signal to noise ratio (19.79), the highest structural similarity index measure (91.7%), and the lowest mean squared error (691.46). The dataset of beef cattle behavior before and after the enhancement of the Lighten Diffusion was sent into the YOLOv11n target detection model for training. The average recognition accuracy of beef cattle behavior increased by 2.5 percentage points after the enhancement. The precision, recall, and mAP@0.5 of the improved CSF-YOLOv11n model reached 93.9, 87.3, and 94.3, respectively, in terms of the target detection. Compared with the Faster-RCNN, RT-DETR, YOLOv5n, YOLOv7, YOLOv8n, YOLOv9t, YOLOv10n, and YOLOv11n, the mAP@0.5 of the model increased by 8.4, 7.4, 5.9, 6.4, 5.6, 6.4, 5.7, and 4.9 percentage points, respectively. The finding can also provide a strong reference to realize the healthy breeding of beef cattle under all weather conditions.