针对建筑工地桩基静载试验中堆载配重块存在边界模糊和形状不规则，采用传统图像分割算法精度不足，严重制约施工安全监控和质量管理效率，提出了一种基于多尺度融合的TransUNet配重块精细分割算法。首先，构建ViT多尺度跳跃连接机制，通过对全局语义特征自适应上采样为三个不同分辨率层级，与局部细节特征建立双重跳跃连接，增强网络对配重块复杂纹理和不规则形状的特征表达问题。其次，在解码器中引入三重注意力机制，通过在空间、通道与深度三重维度的协同增强，提升网络对配重块模糊边界和细小目标检测能力。最后，针对背景与目标类别严重不平衡导致，设计了Dice损失与Focal损失的加权融合的损失函数，提升了模型对小目标石头块的检测能力。通过对真实静载试验平台配重块实验测试，相比原TransUNet，优化后的网络IoU提升了9.6%，有效验证了算法先进性和合理性，具有较高的工程实践意义。

In pile foundation static load tests at construction sites, counterweight blocks often present fuzzy boundaries and highly irregular shapes, which makes conventional image segmentation algorithms insufficient in accuracy and severely limits the efficiency of safety monitoring and quality management. To tackle this challenge, we propose a fine-grained segmentation algorithm for counterweight blocks based on a multi-scale fusion TransUNet framework. Specifically, we first construct a ViT-based multi-scale skip-connection scheme, in which global semantic features are adaptively upsampled to three different resolution levels and then fused with local detail features through dual skip connections, thereby strengthening the network’s feature representation capability for complex textures and irregular shapes of counterweight blocks. Second, a Triplet Attention mechanism is embedded into the decoder to jointly enhance spatial, channel, and depth dimensions, improving the network’s ability to delineate fuzzy boundaries and detect small counterweight blocks. Finally, to address the pronounced class imbalance between background and foreground, we design a weighted hybrid loss that combines Dice loss and Focal loss, further enhancing the model’s sensitivity to small stone blocks. Experiments on real static load test platform images demonstrate that the optimized network improves IoU by 9.6% over the baseline TransUNet, which verifies the effectiveness and superiority of the proposed method and highlights its strong potential for engineering applications.