A real-time ghost machine learning model built on YOLOv8 for traffic road signs detection and classification in Germany.

Identifying traffic signs is an essential part of traffic safety and self-driving systems. In real life, the driving environment is changing, making detecting traffic signs wisely and economically vital. The traffic sign detection problem has several small objects and complex ambient interference. The detecting situation also requires a practical and lightweight detection model. This study proposes a new lightweight model, the enhanced Ghost-YOLOv8, based on lightweight modules GhostConv and C3Ghost, based on the YOLOv8 model. It used a light method to extract the features, significantly speeding up inference. In addition to small, medium, and large objects, the head was expanded to include a new multi-scale feature extraction module layer focused on x-small. The experiment results show that when using the German Traffic Sign Detection Benchmark (GTSDB) dataset with three classes, the enhanced Ghost-YOLOv8 has mAP (0.50) of 99.4%and has fewer computations than the YOLOv8 model by 155.2 GFLOPs and has 18.6 Mparameters, which represents only 27.3% from the parameters used in the base model. Also, we suggested a new dataset called the GTSDB-43 dataset, which expanded the number of classes on the GTSDB dataset from three or four main classes to 43 classes and mentioned their main category type simultaneously. Compared with notable algorithms, this method's accuracy and speed are competitive. [ABSTRACT FROM AUTHOR]