摘要
针对套袋后的葡萄体积增加和葡萄叶片表面积大容易出现重叠遮挡,及人工拍摄视频的速度不稳定可能导致套袋葡萄目标丢失的问题,该研究提出一种基于自纠正NMS(non-maximum suppression)-ByteTrack的套袋葡萄估产方法。该方法首先通过目标检测方法YOLOv5s检测视频中的套袋葡萄,将检测阶段的NMS操作后置到追踪阶段,保留因遮挡而被过滤的果实检测框;其次在ByteTrack的基础上加入相机运动补偿和改进的卡尔曼滤波算法,以自动纠正果实预测框的位置并进行追踪;最后提出一种划线计数策略对套袋葡萄自动计数。试验结果表明,该方法的多目标追踪准确率、多目标追踪精度和ID调和平均数分别为64.6%、82.4%和80.8%,相比ByteTrack分别提高了1.7、1.0和4.1个百分点,平均计数精度达到82.8%。因此,基于自纠正NMS-ByteTrack的估产方法能有效解决套袋葡萄的追踪计数问题,实现对套袋葡萄更精确地估产。
Overlapping occlusion has seriously limited the yield estimation of bagged grapes in recent years,due to the ever-increasing grape volume after bagging and the large surface area of grape leaves.The unstable speed of manual video shooting can also lead to the loss of bagging grape targets.In this study,a yield estimation was proposed for the bagged grape using self-correcting Non-Maximum Suppression(NMS)-ByteTrack.Firstly,the bagged grapes were detected in the video using object detection(YOLOv5s).The NMS operation was also post-positioned to the tracking stage to retain the fruit detection boxes that filtered under the occlusion.Specifically,the detection boxes of bagged grapes were detected by object detection(YOLOv5s),whereas,the prediction boxes of bagged grapes were predicted to calculate the intersection over union using the Kalman filter.If the intersection over the union was less than the given threshold,the detection box was filtered to reserve the detection boxes closer to the predicted position.Then,the final detection box was obtained using the NMS operation.Secondly,the camera motion compensation and improved Kalman filter algorithm were added to automatically correct the position of the fruit prediction boxes and track them using ByteTrack.Specifically,the camera motion compensation was used to first extract the background key points in the bagged grape pictures of the previous frame and the current frame except for the tracking target.The sparse optical flow was used to match the extracted background in the key points of the bagged grapes.Then the affine transformation matrix of background motion was calculated by the RANSAC algorithm,and the Kalman filter was utilized to predict the bagged grape prediction boxes of the current frame.Finally,the affine transformation matrix was obtained to convert the prediction boxes in the coordinate system from the previous to the current frame,in order to realize the position information correction for the prediction boxes of the current frame.In addition,the i
作者
吕佳
张翠萍
刘琴
李帅军
LYU Jia;ZHANG Cuiping;LIU Qin;LI Shuaijun(College of Computer and Information Science,Chongqing Normal University,Chongqing 401331,China;Chongqing Center of Engineering Technology Research on Digital Agriculture&Service,Chongqing 401331,China)
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2023年第13期182-190,共9页
Transactions of the Chinese Society of Agricultural Engineering
基金
重庆市高校创新研究群体(CXQT20015)。
