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 was proposed. In this method， spherical markers were utilized as feature targets and a collaborative framework integrating Channel and Spatial Reliability Tracker with Adaptive Feature Tracking （CSRT-AFT） and Gradient Hierarchical Adaptive Circle Detection （GHACD） algorithm was constructed. Firstly， robust tracking was achieved by CSRT-AFT through a dynamic trajectory filtering and feature adaptation mechanism； an adaptive multi-modal trajectory filtering was designed， and the filtering strategy was switched intelligently based on curvature change rate and acceleration mutation index， so as to suppress intense motion-induced trajectory jitter； dynamic Oriented FAST and Rotated BRIEF （ORB） feature extraction， weighted K-Dimensional （K-D） feature screening， and Least Median of Squares （LMedS） were combined for feature matching and elastic template updating， thereby enhancing feature matching stability under motion blur and complex illumination. Secondly， for rapid and precise target localization， based on using image preprocessing for robustness enhancement， the GHACD 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 was established on the basis of the bridge crane’s workspace. Experimental results demonstrate that this method can detect the load target’s swing angle stably under various trolley speeds and complex conditions including illumination changes and occlusions， and improve detection accuracy and real-time performance significantly.

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

摘要：

针对桥式起重机负载摆角检测中的非匀速运动模糊、光照变化及背景干扰问题，提出一种基于单目视觉负载摆角的实时检测方法。该方法以球形标记为特征目标，并构建通道与空间可靠性自适应特征跟踪器（CSRT-AFT）与梯度分层自适应圆检测（GHACD）算法的协同框架。首先，CSRT-AFT通过动态轨迹滤波与特征自适应机制实现鲁棒跟踪：设计自适应多模态轨迹滤波，并基于曲率变化率与加速度突变指数智能切换滤波策略，抑制剧烈运动的轨迹抖动；结合动态定向快速和旋转BRIEF（ORB）特征提取、加权K维（K-D）特征筛选和最小中值平方法（LMedS）进行特征匹配及弹性模板更新，从而增强运动模糊和复杂光照下的特征匹配稳定性；其次，为了实现目标的快速精确定位，在通过图像预处理增强鲁棒性的基础上，GHACD利用梯度场引导候选圆心生成，采用多阶段概率采样及几何约束验证，实现亚像素级的快速圆检测；最后，结合桥式起重机的工作空间建立负载摆角测量模型。实验结果表明，所提方法在不同小车运动速度和复杂条件如光照变化及遮挡下均能稳定检测负载目标的摆角，并大幅提升了检测的精度与实时性。

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