Infrared Ship Target Detection Algorithm Based on Improved Faster R-CNN
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摘要: 针对Faster R-CNN算法中对于红外舰船目标特征提取不充分、容易出现重复检测的问题,提出了一种基于改进Faster R-CNN的红外舰船目标检测算法。首先通过在主干网络VGG-16中依次引出三段卷积后的3个特征图,将其进行特征拼接形成多尺度特征图,得到具有更丰富语义信息的特征向量;其次基于数据集进行Anchor的改进,重新设置Anchor boxes的个数与尺寸;最后优化改进后Faster R-CNN的损失函数,提高检测算法的整体性能。通过对测试数据集进行分析实验,结果表明改进后的检测算法平均精确度达到83.98%,较之于原Faster R-CNN,精确度提升了3.95%。
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关键词:
- 深度学习 /
- 目标检测 /
- 舰船目标 /
- 红外图像 /
- Faster R-CNN
Abstract: To solve the problem of insufficient feature extraction and repeated detection of infrared ship targets by the Faster R-CNN algorithm, a ship target detection algorithm based on an improved Faster R-CNN is proposed. First, three feature graphs are drawn from the backbone network, VGG-16, after a three-segment convolution, and the features are spliced to form a multi-scale feature graph to obtain a feature vector with richer semantic information; second, the Anchor is improved based on the dataset, and the number and size of the Anchor boxes are reset; finally, the loss function of the improved Faster R-CNN is optimized to improve the feature extraction ability of the target. An analysis of the experimental results on the test dataset demonstrates that the average accuracy of the improved detection algorithm was 83.98%, which is 3.95% higher than that of the original Faster RCNN.-
Key words:
- deep learning /
- target detection /
- ship target /
- infrared image /
- Faster R-CNN
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表 1 分类结果判别表
Table 1. Classification result discriminant table
Real situation Discriminant result Positive example Counter example Positive example TP(True positive example) FN(False Counter example) Counter example FP(False positive example) TN(True Counter example) 表 2 改进前后算法性能对比
Table 2. Comparison of algorithm performance before and after improvement
Model name AP/% mAP/% Time/s Faster R-CNN 80.03 80.03 0.3128 Improved Faster R-CNN 83.98 83.98 0.3384 -
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