摘要
针对图像实时采集时,由于镜头缺陷、相机抖动、目标运动等原因造成的模糊图像输入,导致训练完成的深度学习模型检测准确率下降问题,本文提出一种基于改进Faster R-CNN和SSRN-DeblurNet的两阶段检测方法,用于农田环境模糊图像中的障碍物检测。第1阶段进行锐度评价和去模糊处理,利用简化尺度循环网络(Simplified scale recurrent networks,SSRNDeblurNet)对模糊农田图像进行去模糊。第2阶段进行障碍物检测,在原有的Faster R-CNN网络中添加了候选区域优化网络来提高区域候选网络中的目标区域质量。在自制的模糊数据集上,利用所提出的两阶段检测方法对8种农田障碍物进行检测。与原始Faster R-CNN相比,两阶段检测方法的平均精度均值(mAP)提高了12.32个百分点,单幅图像的平均检测时间为0.53 s。所提出的两阶段方法能有效减少模糊农田图像中障碍物的误检和漏检,满足拖拉机低速作业的实时检测需求。
When it is in real-time image acquisition,image blurring caused by lens defects,camera jitter,target movement and so on will result in poor precision of target detection by using the trained deep learning model.Here,a two-stage detection model based on an improved Faster RCNN and an SSRN-DeblurNet was proposed to perform obstacle detection for blurred farmland images.In the first stage,sharpness evaluation and deblurring were carried out,and the simplified scale recurrent networks(SSRN-DeblurNet)was used for deblurring of blurred farmland images.In the second stage,obstacle detection was implemented by using the improved Faster R-CNN which was added a proposal region optimization network to improve the quality of the regions in the region proposal networks.Then,the proposed two-stage detection model was used to detect eight types of farmland obstacles with self-made blurred dataset.Compared with the original Faster R-CNN,the mean average precision(mAP)value was increased by 12.32 percentage points,and the average detection time of a single image was 0.53 s.The results showed that the proposed two-stage model can not only effectively reduce the false detection and missing detection of obstacles in blurred farmland images,but also can meet the real-time detection requirements of tractors operating at low speeds.
作者
薛金林
李雨晴
曹梓建
XUE Jinlin;LI Yuqing;CAO Zijian(College of Engineering, Nanjing Agricultural University, Nanjing 210031, China)
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2022年第3期234-242,共9页
Transactions of the Chinese Society for Agricultural Machinery
基金
江苏省科技计划项目(BK20151436)
江苏高校“青蓝工程”项目。
作者简介
薛金林(1974—),男,教授,博士,主要从事农业车辆测控技术和智能化研究,E-mail:xuejinlin@njau.edu.cn。
