在目标检测中, 通常使用候选区域提高目标的检测效率。为解决当前候选区域质量较低的问题, 本文将卷积边缘特征、显著性及目标位置信息引入到候选区域算法中。首先, 利用卷积神经网络将待检测图像生成更富有语义信息的边缘特征, 并通过边缘点聚合及边缘组相似性策略, 获取每个滑动窗口的边缘信息得分; 其次, 利用显著性目标的局部特征, 统计每个滑动窗口中的目标显著性得分; 第三, 根据目标可能出现的位置, 计算每个滑动窗口中的目标位置信息得分; 最后, 利用边缘信息、显著性及位置信息的分数确定候选区域。在PASCAL VOC 2007验证集上进行实验, 给定10 000个候选区域, 交并比取0.7时, 所提算法的召回率为9050%, 较Edge Boxes算法提高了3%。每张图像的运行时间大约为076 s。结果表明, 本文算法可快速产生较高质量的候选区域。

Region proposals are usually used to efficiently detect objects in object detection. In order to solve the problem that the region proposals have low quality, the convolutional edge features, object saliency and position information of objects are introduced into the region proposals algorithm. Firstly, the edge features with semantically meaningful information are generated from the images to be detected using the convolutional neural networks, and the score of edge information for per sliding window is obtained through the strategy of edge clustering and the similarities between the edge groups. Then, the salient object scores of each sliding window are computed using the local features of salient objects. Thirdly, the scores of object position information are calculated according to the location where objects may occur. Finally, the region proposals are determined by three components including edge information scores, salient object scores and the object positions scores. The experimental results in PASCAL VOC 2007 validation set show that given just 10 000 region proposals, the object recall of the proposed algorithm is 9050%, that is increased by 3% comparing with Edge Boxes with intersection over union threshold of 07. The run time of the proposed method is about 076 seconds for processing one image, and this demonstrates that our approach can yield a set of region proposals with higher quality at a fast speed.