Online Detection System for Crushed Rate and Impurity Rate of Mechanized Soybean Based on Deeplabv3+

Abstract

In this study, an online detection system of soybean crushed rate and impurity rate based on DeepLabV3+model was constructed. Three feature extraction networks, namely the MobileNetV2, Xception-65, and ResNet-50 models, were adopted to obtain the best DeepLabV3+model through test analysis. Two well-established semantic segmentation networks, the improved U-Net and PSPNet, are used for mechanically harvested soybean image recognition and segmentation, and their performances are compared with the DeepLabV3+ model’s performance. The results show that, of all the models, the improved U-Net has the best segmentation performance, achieving a mean intersection over union (FMIOU) value of 0.8326. The segmentation performance of the DeepLabV3+ model using the MobileNetV2 is similar to that of the U-Net, achieving FMIOU of 0.8180. The DeepLabV3+ model using the MobileNetV2 has a fast segmentation speed of 168.6 ms per image. Taking manual detection results as a benchmark, the maximum absolute and relative errors of the impurity rate of the detection system based on the DeepLabV3+ model with the MobileNetV2 of mechanized soybean harvesting operation are 0.06% and 8.11%, respectively. The maximum absolute and relative errors of the crushed rate of the same system are 0.34% and 9.53%, respectively.