## 5 Conclusions  
In this study, to solve the problem of the difficulty of detecting leg diseases in broiler chickens, we proposed a system for assisting in measuring the length of the tibia in broilers' legs. By observing changes in tibia length in real time, leg diseases in broiler chickens can be eliminated and prevented. In order to save effective computing resources in practical applications, we did not introduce too many parameters. We used the CARAFE upsampling operator, integrated a parallel network attention mechanism (ParNetAttention) into the Backbone, and incorporated the efficient multi-scale attention mechanism into multiple channels within the Neck. We proposed the Tibia-YOLO tibia detection model, which was combined with a subsequent SVR regression model to measure the length. Tests showed that, on the Tibia dataset, \(mAP_{0.50.95}\) increased by 6.1%. It increased on both the VOC2012 and COCO datasets, and the improved model did not cause too much of an increase in the number of parameters and GFLOPS. In the regression model that was fitted using the test results, the mean square error (MAE) and root mean square error (RMSE) were 2.77 mm and 3.37 mm, respectively. The performance was very good, and the tibia length of live broilers could be accurately measured. This can help the broiler chicken breeding industry detect the occurrence of broiler leg diseases more efficiently. Compared with the traditional measurement method that uses vernier calipers after dissection or a tape measure in vivo, our method improved the measurement accuracy and efficiency and could better protect broilers. Although the performance of the Tibia-YOLO model was good, there were some unexpected problems in the test set of the Tibia dataset. Certain broilers exhibited increased activity during DR image capture, resulting in instances of edge blurring in the captured images. In such cases, the Tibia-YOLO model struggled to accurately discern the positional relationships of tibias, leading to imprecise bounding box detection. This, in turn, affected subsequent length prediction tasks, necessitating an enhancement in the detection accuracy of the Tibia-YOLO model. Acknowledging the limitations of the approach, we endeavored  
Figure 9: Scatter plot of predicted results and true values for nine different regression models. In the figure, the x-axis represents the number of pixels in units and the y-axis represents the broiler tibia length in millimeters. Some labeled coordinates are displayed within the coordinate axis, representing the actual tibia length value and the predicted tibia length value. to diversify the broiler selection as much as possible during the establishment of the Tibia dataset. This involved capturing images of broilers from various breeds, ages, and weights to augment the model's generalization capabilities. Presently, the method has not been widely adopted for measuring other parts of broilers. Furthermore, it has not gained popularity for application to other poultry species, such as ducks and rabbits. Future endeavors in this work will require the incorporation of a broader spectrum of poultry categories and diverse anatomical detection sites, paving the way for more advanced auxiliary detection technologies in poultry breeding facilities. S.S. and J.Y. ; Methodology, S.S. and J.Y. ; Validation, Z.C. ; Writing - original draft, S.S. and J.Y. ; Writing - review and editing, Z.C. and J.L. ; Visualization, S.S. and J.Y. ; Project administration, R.S. ; Funding acquisition, R.S. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Key Research and Development Program of China (grant number 2021 YFD1300101). All animal procedures were approved by the Experimental Animal Welfare and Animal Experiment Ethics Review Committee, China Agricultural University. Not applicable. The Tibia dataset is not publicly available due to the data being part of ongoing research.