基于改进 SSD 网络的目标检测方法

传统目标检测方法存在准确率低、可靠性差、效率低等问题，无法满足对大量图片准确、高效处理的需求。对 SSD 网络结构进行改进，删除原网络最后两个预测层，对保留各预测层的默认框个数和宽高比进行优化，同时对保留的最后一个预测层的网络参数进行改进。改进后的 SSD 网络减少了网络参数和计算量，对存在遮挡、目标较小等情况的图片数据具有更好的检测精度和检测效果，同时模型检测的 mAP 提高了约 5.1%。改进后的网络模型解决了传统方法的不足，可以实时、准确、高效地对大量图片数据进行目标检测处理。

Target Detection Methods Based on Improved SSD Network

Traditional target detection methods are confronted with some problems，such as low accuracy，poor reliability and low efficiency，which make the methods fail to meet the needs of accurate and efficient processing of large numbers of pictures. This paper improves the structure of SSD network，deletes the last two prediction layers of the original network，optimizes the number of default frames and the aspect ratio of each prediction layer，and improves the network parameters of the last prediction layer. The improved SSD network reduces the network parameters and computational load，and has better detection accuracy and detection effect for images with occlusion and small target. Meanwhile，the mAP of model detection is increased by about 5.1% . The improved network model solves the shortcomings of traditional methods and can detect and process large number s of image data accurately and efficiently in real time.