【摘要】 SSD(Single Shot MultiBox Detector)是一种基于深度学习的目标检测算法,它作为当前最为主流的检测算法之一,在极大地提高检测速度的同时,还能保证一定的检测精度,但是仍难以满足实际应用的需求。本文在SSD模型的基础上,引入注意力机制,提出一种基于SSD改进的目标检测算法。注意力机制能够有效地提高卷积神经网络对图片特征的提取能力,从而进一步提高算法的检测精度。改进后的算法在Pascal VOC数据集上进行对比试验。实验结果表明,改进后的模型在Pascal VOC2007测试集上的检测精度达到78.5%mAP(mean Average Precision),比改进前提高4.2个百分点,在Pascal VOC2012测试集上的检测精度达到77.1%mAP,比改进前提高4.7个百分点。更多还原

【Abstract】 Single Shot MultiBox Detector(SSD), one of the most prevalent object detection algorithms based on deep learning, greatly shortens the time of object detecting, whose accuracy of detection is acceptable. However its accuracy cannot meet the need of practical application. Attention mechanism is helpful in improving the performance of convolutional neural network and the accuracy of detection. This paper modifies SSD with attention mechanism and proposes a modified object detection algorithm based on SSD. The modified SSD has been tested with Pascal VOC dataset. The modified SSD achieves 78.5% mAP in Pascal VOC2007 test set and 77.1% mAP in Pascal VOC2012 test set, which are higher than original SSD 4.2 percentage points and 4.7 percentage points separately.更多还原