Improved traffic light detection algorithm for YOLOv8

The implementation of traffic light detection algorithms is a crucial aspect for the mobility of blind individuals. However, the existing algorithms present limitations in terms of detection efficiency, cost, and applicability to mobile devices. In response to these challenges, a novel lightweight traffic light detection algorithm has been proposed, which enhances the YOLOv8 algorithm. Firstly, a two-layer routing attention mechanism is introduced into the end of the backbone network and the neck network to strengthen the feature extraction capability and suppress the interference of irrelevant features. Secondly, the C2fGhost module is used in the neck network of YOLOv8 in order to reduce the amount of floating-point computation in the fusion process of the feature channels and lower the number of model parameters, while improving the feature expression performance. The experimental results demonstrate that the enhanced algorithm yields an mAP50 improvement of 9.3% on the S2TLD dataset. It also reduces the number of model parameters by 6.7% to 2.7 million. The algorithm achieves a detection speed of 102 FPS, enabling real-time detection of traffic signal targets. <br/> <br/> This method has been demonstrated to be effective and superior to other mainstream target detection algorithms through a comparison of their respective results.