Authors

Abstract

In order to quickly and accurately identify the corn ears lost during the corn harvesting process, a corn ear loss detection method based on the improved Mask-RCNN model was proposed. The lost corn ears in the field were taken as research objects, the images of the lost corn ears were collected and the fallen ears data set was established. The size ratio of the Anchor Box of the area recommendation network was changed by changing the K-means algorithm to reduce the influence of artificial setting intervention. The group convolution was introduced into the residual unit and the channel dimension was divided into 3 equal parts to reduce the model parameters in the basic feature extraction network ResNet. A Convolutional Block Attention Module (CBAM) was introduced to improve the accuracy of the model in the last layer of the ResNet network. Results showed that the average target recognition accuracy of the method on the test set in this study was 94.3%, which was better than that of the previous model, and the average time to recognize a single image was 0.320 s. The proposed method could detect the lost corn ears during the harvesting process under the complicated background, and provide a reference for the corn ear loss detection of the corn harvester.

Abstract in Chinese

为快速准确识别玉米收获过程果穗损失，本文以玉米收获机田间收获掉落的果穗为研究对象，进行图像采集并建立掉落果穗数据集，提出一种基于改进Mask-RCNN的果穗损失检测方法。将Mask-RCNN深度学习模型引入玉米果穗图像识别中并提出一种优化方法，通过K-means算法改变区域建议网络Anchor Box尺寸比例以减少人为设置干预影响，在基础特征提取网络ResNet中将分组卷积引入残差单元并将通道分为3等份以减少模型参数，在ResNet网络层最后一层引入卷积注意力模块(CBAM)以提高模型的准确率。结果表明：本文方法在测试集上平均目标识别准确率为 94.3%，优于改进前的模型，识别单幅图像的平均耗时为 0.320 s。所提方法对复杂背景下玉米收获机掉落的果穗有较好的检测效果，可为玉米收获机掉穗损失检测提供参考。