野外复杂环境下地线检测是高压输电线路巡检机器人自主越障的关键技术之一,光照变化和地线表面新旧程度是影响架空地线检测准确率的关键因素。针对这一问题,提出一种基于离线学习的野外架空地线检测算法。离线训练阶段,首先采用自适应同态滤波器对输入样本进行光照补偿,然后提取样本的局部二值模式直方图特征,结合离线学习算法支持向量机训练得到二类分类器;在线检测阶段,首先对样本分块,分类得到候选地线样本块,然后采用随机采样一致性算法去除误检块,拟合得到地线在图像坐标系中的位置参数。在野外新旧程度不同的线路上进行的大量实验表明,该方法对光照变化有良好的适应性,能较准确地检测不同新旧程度的架空地线,为后续的地线空间定位与抓线控制奠定了基础。

Overhead ground wire detection under complex field environment is one of the key technologies of automatic obstacle surmounting for a high voltage transmission line inspection robot. The varying illumination and wire surface condition are the key factors affecting the detection accuracy. To address the problem, a field wire detection algorithm based on off-line learning is proposed. In the training phase, the adaptive homomorphic filter is applied to the input samples to compensate illumination, followed by local binary pattern histogram feature extraction and support vector machine training to get a binary classifier. In the on-line detection phase, each sample is divided into patches, followed by classification through the trained classifier to get the wire patch candidates. Then, the random sample consensus algorithm is adopted to remove mistakenly identified patches, and the remaining candidates are fitted into a line to get the wire parameters in the image coordinate system. The results of a number of experiments with field surroundings and different wires show that the proposed method has good adaptability to varying illumination and can detect both old and new wire accurately. Furthermore, this work has laid a solid foundation for the subsequent three dimensional positioning and grasping control of the ground wire.