基于YOLOv3与分水岭的直升机桨叶欠曝光图像圆形标记点检测

针对现有的直升机桨叶欠曝光图像中圆形标记点检测方法存在自适应能力不强、速度慢、精度不高的问题，提出了基于YOLOv3（you only look once）与分水岭的直升机桨叶欠曝光图像圆形标记点检测方法.首先，将采集的真实桨叶欠曝光图像中的圆形标记点进行标注后，制作成数据集，并训练YOLOv3网络；其次，用训练好的YOLOv3网络检测出圆形标记点区域；再次，改进传统分水岭标记提取方式，采用多线程技术并行在各圆形标记点区域内进行分水岭变换，得到圆形标记点边缘检测结果；最后，采用最小二乘圆拟合和奇异点去除法实现圆形标记点的精确定位.研究者通过对多幅欠曝光桨叶图像中圆形标记点进行检测实验，验证了该方法具有自适应能力强、速度快、精度高的优点，并已将其用于直升机桨叶欠曝光图像圆形标记点的检测.

Circular Marker Detection of Under-Exposed Images of Helicopter Blades Based on YOLOv3 and Watershed

A method of circular marker detection in under-exposed helicopter blade images based on YOLOv3 (you only look once) and watershed algorithm is proposed, aiming to improve detection adaptability, speed up the detection and obtain accurate position of circular markers. Firstly, the real under-exposed blade images are labeled for dataset making, on which the YOLOv3 network is trained. Secondly, the circular marker regions in blade images from testing dataset are detected by the trained YOLOv3 network. Thirdly, the traditional watershed marker detection method is improved, and the circular marker regions are used for the watershed transformation by the multi-threading technology in parallel, and the edge detection results of the circular marker are obtained. Finally, the circular markers are accurately located by the least square circle fitting and the method of removing the singular points. The proposed method is proved to be adaptable, fast and accurate by a number of experiments in many under-exposed helicopter blade images, and has been applied to circular marker detection in under-exposed helicopter blade images.