一种应用于光遗传激光投影系统的目标检测算法

Object Detection Algorithm Applied to Optical Genetic Laser Projection System

设计了一种用于无线光传输的激光投影系统并提出了一种基于深度学习的改进型YOLOv3(you only look once,v3)网络用于检测小鼠图像的位置。该网络使用分组卷积对网络参数进行压缩以提高目标检测速度,使用通道混洗方法以增强网络的信息流通能力。利用交叉熵损失函数中的两个超参数来调整正、负样本的比例以降低易分类样本在损失函数中的权值,提高了目标检测精度。在PASCAL VOC2007和自制小鼠图像数据集上分别进行实验,结果表明提出的基于改进型YOLOv3网络的检测算法检测精度达90.3%,检测速度和检测精度都优于传统型网络结构。应用该算法的激光投影系统可以实时检测运动小鼠目标并进行无线光传输等光遗传实验。

A laser projection system for wireless light transmission is designed and an modified YOLOv3(you only look once,v3)network based on deep learning is proposed to detect the location of mouse images.The network first uses packet convolution to compress network parameters to increase target detection speed,and then uses channel shuffle to enhance the network′s information flow capabilities.The ratio between the positive sample and the negative sample is adjusted by two hyperparameters on the cross entropy loss function to reduce the weight of the easily classified sample in the loss function,and the detection accuracy is improved.The experimental results on the PASCAL VOC2007 and the self-made mouse image datasets show that the proposed detection algorithm based on the improved YOLOv3 network has a detection accuracy of 90.3%,which is superior to the traditional network structure in terms of detection speed and detection accuracy.The laser projection system using the algorithm can detect moving mouse targets in real time and perform optogenetic experiments such as wireless light transmission.