LAUSR

It is difficult to extract small and dense objects with random state, such as grain and impurity, in image of vehicle-mounted dynamic rice grain flow on combine harvester. Therefore, this paper improves Deeplabv3+ by constructing MobileNetv2 in coding layer and adding ECA(Efficient Channel Attention) to Encoder and Decoder to improve extraction accuracy of high-dimensional features in images with a large number of objects with random state. In addition, the YOLOv4 is improved by using Mixup in preprocessing, constructing Mish in Neck and Head, adding ECA to Neck and Prediction of BackBone to improve training precision of small and dense objects and reducing effect of gradient disappearance. And the impurity/breakage rates are assessed based on relationship model between pixel area and quality, improved Deeplabv3+ and YOLOv4. The proposed method was verified by experiments with images acquired on intelligent combine harvester. Compared with existing Deeplabv3+, YOLOv4, U-NET, BP, the extraction accuracy by improved method increased by more than 4.01%. The average relative error and time of impurity/breakage assessment by proposed method were 7.69% and 1.56s. The proposed method can accurately and rapidly assess impurity/breakage rates for dynamic rice grain flow on combine harvester, and further realize closed-loop control of intelligent harvesting operation.