An improved Faster R-CNN network for aeroengine fuse fracture detection
首发时间:2020-12-18
Abstract:In order to meet the needs of aeroengine fuse fracture detection in practical application, an improved Faster R-CNN small target detection network is proposed. Firstly, FPN feature graph pyramid is added to improve the extraction ability of small target features, and then ROI Align is used to replace ROI pooling to reduce the loss of feature information of small targets. Experiments on the fuse fracture data set show that the improved detection network is 5.76% higher than Faster R-CNN on mAP. The experimental results show that the improved network is more advanced and has a practical application prospect in aeroengine fuse fracture detection based on computer vision.
keywords: Image recognition Faster R-CNN Small target detection
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改进的Faster R-CNN网络用于航发保险丝断裂检测
摘要:为了满足航空发动机保险丝断裂检测实际应用的需要,提出了一种改进的Faster R-CNN小目标检测网络。首先通过增加FPN特征图金字塔来提高小目标特征的提取能力,然后用ROI Align代替ROI pooling,减少小目标特征信息的丢失。在航空发动机保险丝断裂数据集上的实验表明,改进后的检测网络在mAP指标上比Faster R-CNN提高了5.76%。实验结果表明,改进后的网络具有更高的先进性,在基于计算机视觉的航空发动机保险丝断裂检测中具有实际应用前景
关键词: 图像识别 Faster R-CNN 小目标检测
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