激光与光电子学进展, 2019, 56 (1): 011002, 网络出版: 2019-08-01
基于改进SSD的实时检测方法 下载: 1998次
Real-Time Detection Based on Improved Single Shot MultiBox Detector
图像处理 深度学习 目标检测 卷积神经网络 实时检测 image processing deep learning object detection convolutional neural network real-time detection
摘要
教育部-中国移动科研基金项目、青海省高端创新人才计划;
Abstract
In recent years, the convolutional neural networks are widely used in the field of object detection. However, these methods based on convolutional neural networks require a large amount of calculations, so that it is difficult for these methods to run on platforms with limited computation. A fast object detection method is proposed based on single shot multibox detector (SSD), namely Faster-SSD. The method realizes the real-time detection and high accuracy with limited computation. The basic network of SSD is replaced with ResNet-34. In the stage of generating the prediction frame, first obtain the prior boxes which satisfy the condition, and then generate the prediction frame of the corresponding category. The variable minimum threshold is proposed to reduce the amount of computation. Finally, the online hard example mining is applied to remove the simple samples. Experimental results show that the Faster-SSD gets 14 frame/s on NVIDIA Jetson TX2.
陈立里, 张正道, 彭力. 基于改进SSD的实时检测方法[J]. 激光与光电子学进展, 2019, 56(1): 011002. Lili Chen, Zhengdao Zhang, Li Peng. Real-Time Detection Based on Improved Single Shot MultiBox Detector[J]. Laser & Optoelectronics Progress, 2019, 56(1): 011002.