Authors

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.

Abstract in Chinese

为了实现大豆机械化收获破碎率和含杂在线检测，本研究构建了基于DeepLabV3+的在线检测系统。采用三种特征提取网络（MobileNetV2、Xcepton-65和ResNet-50模型）测试分析获得最佳的DeepLabV3+模型。并引入改进的U-Net和PSPNet，评估DeepLabV3+模型的性能。结果表明，在所有模型中，改进的U-Net具有最佳的分割性能，实现了0.8326的平均交合（FMIOU）值。基于MobileNetV2的DeepLabV3+模型的分割性能与改进的U-Net相似，FMIOU为0.8180。基于MobileNetV2的DeepLabV3+模型分割大豆图像速度为168.6ms。以人工检测结果为基准，基于MobileNetV2的DeepLabV3+模型检测大豆含杂率的最大绝对误差和相对误差分别为0.06%和8.11%，破碎率的最大绝对误差和相对误差分别为0.34%和9.53%。