To address the challenges faced by stamp character detection on metal products, such as character tilt, blurriness, inconsistent fonts, and interference from rust stains, a character detection model based on adaptive feature fusion, named YOLO-CHAR, is proposed. This model employs the MobileNet feature extraction network to dynamically adjust the weights of channel features, enhancing the model’s ability to capture key features. At the feature fusion layer, it utilizes the generalized feature pyramid network(GFPN) structure and the simplified attention module(SimAM) attention mechanism to flexibly capture multi-scale features and strengthen feature fusion capabilities. Based on this character detection model, a low-quality train wheelsets stamp character detection and recognition system is designed and implemented. This system has been put into use, achieving an overall daily average recognition accuracy of over 92% for wheelsets, which meets the on-site operational requirements.
