两级上下文卷积网络宽视场图像小目标检测方法

目标检测和识别已经在输电线路巡检中被广泛采用。由于图像数据量大,小目标分辨率低,现有的图像金字塔、特征金字塔和多异构特征融合等方法虽能准确地检测目标,却非常耗时,因而快速、准确地检测宽视场图像中小目标仍是一个挑战。此算法提出一个两个Faster-RCNs级联的上下文宽视场小目标检测卷积网络,首先,针对降分辨率的宽视场图像,利用一个Faster R-CNN来检测目标的上下文区域,然后,针对上下文区域对应的高分辨率原始图像,利用Faster R-CNN来检测来小目标。我们用航拍输电线路图像数据集进行了目标检测试验,试验结果表明,小目标检测方法达到了88%的检测精度,比单级Faster R-CNN检测方法具有更高的准确率。

Two-stage context convolutional network for small target detectionin wide-view-field images

Object detection and recognition has been widely applied to power transmission line inspection. Existing methods, such as multi-scale image pyramid, multi-scale feature pyramid and multiple heterogeneous feature fusion, etc. can detect small objects accurately, but usually require heavy computational burden, thus fast and precise target detection in wide-view-field images is still challenging due to large amount of image data and low resolution of small targets. In this paper, we propose a two-stage context convolutional network for small target detection in wide-view-field images, which consists of two cascaded Faster R-CNNs, the first Faster R-CNN is used to locate context regions in a low resolution image, and another Faster R-CNN to detect small targets in high-resolution images ofSdetected context regions. We test the proposed method is test on our datasets captured by unmanned aircraft, experimental results show that the proposed method could lead to 88% accuracy for small target detection and is higher than that of the one-stage Faster R-CNN.