With the increasing demand for traffic sign detection, the challenge of small target detection has become particularly prominent. The present study proposes an innovative approach by integrating knowledge distillation, L2 loss function, and convolutional block attention module (CBAM) mechanism to effectively tackle this issue. This series of improvements not only provide a new idea for small target detection, but also bring significant performance improvement in actual traffic scenes. Then, the integration method of the bidirectional feature pyramid network (BiFPN) is used to enhance the flexibility of the neural network to deal with input of different scales, while speeding up and improving the process of feature fusion. The experimental results demonstrate that when processing the Chinese city traffic sign detection benchmark (CCTSDB) dataset and executing the FLOW-IMG small target detection task, the optimized algorithm shows obvious performance improvement, and its accurate recognition rate jumps to 97% and 84.9%, respectively. For the basic algorithm, two datasets achieved improved accuracy by an innovative approach, improving accuracy by 5.8% and 1.3%, respectively. In terms of resource efficiency, compared to the original teacher model, the newly constructed model reduced the number of computing participants by approximately 15% during execution, while successfully reducing the overall computing task load by 14%.