关键词: 端到端检测与识别, 旋转文本检测, 工业文本读取, 机油标签, 工业视觉检测

Abstract: To address the problems of small scale, reflection, multi-angle orientations, and background texture interference in industrial oil-label specification strings, as well as the difficulty of conventional two-stage methods in balancing end-to-end detection-recognition accuracy and system efficiency, an end-to-end oil label text detection and recognition method was proposed. Firstly, a detection-recognition dual-stream decoupling structure was built to reduce feature competition between text localization and character recognition. Secondly, a prediction-driven differentiable rotated cropping module was designed to align rotated candidate regions and generate recognition inputs. Finally, a stable joint training strategy was introduced to improve end-to-end optimization. On the self-built oil label dataset under the Strict-E2E protocol, the end-to-end precision, recall, F1 score, and system speed were 93.75%, 85.49%, 89.43%, and 49.70 FPS, respectively. Compared with Pipeline-SAR, which has a high end-to-end F1 score, the proposed method improves the F1 score by 0.13 percentage points and increases the system speed from 7.44 FPS to 49.70 FPS. Field test results show that the proposed method can support industrial-camera streaming, model verification, and status determination, making it suitable for online reading and production-line verification of oil-label specification strings.

Key words: end-to-end detection and recognition, rotated text detection, industrial text reading, oil label, industrial visual inspection