## 4 Experimental Analysis
### Experimental Configuration and Evaluation Metrics  
The experiment was conducted using the Windows 10 operating system with hardware comprising a GeForce RTX 3090 GPU and an AMD Ryzen 9 5900X 12-Core Processor. Programming was performed using the Python language and the PyTorch deep learning framework. The model training employed the Adam optimization algorithm, setting the initial learning rate (lr) to 0.001. The model's loss function adopted the contrastive loss function with the RELU function serving as the activation function for the hidden layers. The batch size was set to 20, and the model underwent a total of 30 epochs during training. To ensure the optimal bone stick rejoining model, the training retained the training weights of the model at each iteration, subjected the test set to evaluation, and selected the experiment's best-performing iteration for comparative analysis. In order to maximize the feature extraction capability of the backbone feature extraction network, under the selection of Resnet50 as the base network, we use the idea of transfer learning to use the initial weights pre-trained in the Image Net dataset and then further train them in the bone stick dataset for fine-tuning. Bone stick rejoining is defined as follows: given a broken bone stick image as input, the algorithm returns the top-T bone stick images with the highest match to this image as a candidate rejoining set. This set should include bone stick images that can genuinely be rejoined with the input image among the T images. The rejoining is considered satisfactory if this condition is met. In the constructed CBSD dataset, the true rejoined objects for each image are known, allowing the algorithm to automatically calculate its Top-T accuracy without the involvement of experts. However, in real-world scenarios of bone fragment rejoining, since the true rejoining objects of a given bone stick image are not known in advance, bone stick experts are required to use their professional knowledge to filter and confirm the candidate rejoining results. If the value of T is large, there will be too many candidate results, and reviewing the matching results of each bone stick fragment image will consume a significant amount of time for the bone stick experts. Additionally, due to the large number of bone stick images, it becomes challenging for bone stick scholars to complete the filtering and confirmation of candidate rejoining results for all bone stick fragment images. In order to effectively alleviate the workload of bone stick experts in real-world bone fragment adhesion scenarios, and based on the recommendations of bone stick experts, the value of T is set to 15. Therefore, the evaluation metrics are defined as follows:  
Top-\(T\) rejoining accuracy (\(ACC\)) is the ratio of the number of images that can correctly find the concatenation result to the number of all images that actually have rejoining objects  
Figure 7: Dataset production In the figure, 27356_01_01 and 27356_02_01 denote the acquired local region images of bone sticks, while 27356_01_02 and 27356_02_02 represent the corresponding edge contour images. in the case of returning the Top-\(T\) candidate images with the highest rejoining match for each image. Then, the rejoining accuracy \(ACC\) can be expressed as Equation (9). \[ACC=\frac{TP}{M} \tag{9}\]  
where \(TP\) denotes the number of correctly predicted images, and \(M\) represents the total number of images. \[\text{Missed detection rate}=1-\text{Accuracy};\]  
Precision refers to the number of correct positive identifications divided by the sum of the rankings of the true positive identifications among the Top-\(T\) candidate results. If two algorithms find the same number of correct positive identifications, their Top-\(T\) accuracy and missed detection rate are also the same. In such cases, if a method's correct positive identifications are consistently ranked higher among the Top-\(T\) results, its precision value is higher.