A more effective directed text detection algorithm is proposed for the problem of low accuracy in detecting text with multiple sources, dense distribution, large aspect ratio and arbitrary alignment direction in the industrial intelligence process. The algorithm is based on the YOLOv5 model architecture, inspired by the idea of DenseNet dense connection, a parallel cross-scale feature fusion method is proposed to overcome the problem of blurring the underlying feature semantic information and deep location information caused by the sequential stacking approach and to improve the multiscale feature information extraction capability. Furthermore, a rotational decoupling border detection module, which decouples the rotational bounding box into horizontal bounding box during positive sample matching, is provided, overcoming the angular instability in the process of matching the rotational bounding box with the horizontal anchor to obtain higher-quality regression samples and improve the precision of directed text detection. The MSRA-TD500 and ICDAR2015 datasets are used to evaluate the method, and results show that the algorithm measured precision and F1-score of 89.2% and 88.1% on the MSRA-TD500 dataset, respectively, and accuracy and F1-score of 90.6% and 89.3% on the ICDAR2015 dataset, respectively. The proposed algorithm has better competitive ability than the SOTA text detection algorithm.