关键词: 桥式起重机, 单目视觉, 自适应算法, 图像处理, 摆角测量, 模糊图像

Abstract: Aiming at the problems of non-uniform motion blur, illumination changes, and background interference in the detection for load swing angle of bridge cranes, a real-time monocular vision-based method for load swing angle detection is proposed. This method utilizes spherical markers as feature targets and constructs a collaborative framework integrating a Channel and Spatial Reliability Tracker with Adaptive Feature Tracking (CSRT-AFT) and a gradient-layered adaptive circle detection algorithm. The CSRT-AFT achieves robust tracking through a dynamic trajectory filtering and feature adaptation mechanism. This includes an adaptive multi-modal trajectory filtering strategy, intelligently switched based on curvature change rate and acceleration mutation index to suppress severe motion trajectory jitter. It also combines dynamic Oriented FAST and Rotated BRIEF (ORB) feature extraction, weighted K-Dimensional (K-D) feature screening, and the least median of squares method for feature matching and elastic template updating, enhancing feature matching stability under motion blur and complex illumination. For rapid and precise target localization, building upon image preprocessing for enhanced robustness, the gradient-layered adaptive circle detection algorithm achieves sub-pixel level fast circle detection through gradient field-guided circle center candidate generation, multi-stage probability sampling, and geometric constraint verification. Finally, a load swing angle measurement model is established in conjunction with the bridge crane's workspace. Experimental results demonstrate that this method can stably detect the load target's swing angle under various trolley speeds and complex conditions including illumination changes and occlusions, significantly improving detection accuracy and real-time performance.

Key words: bridge crane, monocular vision, adaptive algorithm, image processing, swing angle measurement, blurred images