Research on improved YOLOv8n-based traffic sign detection algorithm

In response to the inefficiency, subjectivity, and traffic disruption caused by manual visual identification in current road maintenance department road signs detection, An improved YOLOv8n road signs detection algorithm has been proposed by this paper. The algorithm introduces the concept of dynamic convolution, designing and constructing a C2f-Dynamic module that enhances the parameter amount in C2f using dynamic convolution to strengthen the learning ability of small-scale networks on large-scale data. Furthermore, to strengthen the network's feature extraction capability and reduce interference from complex backgrounds, The LSKA attention mechanism is introduced in this paper. Lastly, To address the original model's limitations in detecting small targets, a P2 detection layer is incorporated into the neck of the network. The results demonstrate that on the CCTSDB-2021 dataset, the enhanced algorithm attained precision, recall, and mAP50 of 88.2%, 72.1%, and 80.1% respectively, which are 0.2%, 2.3%, and 4.1% higher than the original YOLOv8n algorithm, operating at a frame rate of 322.6 FPS. In summary, the algorithm has significantly higher precision than the original algorithm and can satisfies the real-time detection requirements, which is of great practical value for road maintenance departments.