Objective  Corn silk is the pollination organ of maize, its growth state will affect the yield of corn. In order to identify the corn silk in real-time and accurately in corn growth state monitoring and yield prediction, a corn silk detection model based on multi-feature fusion SSD (MF-SSD) convolutional neural network was proposed.

Method  Corn silk detection was based on feature images. The MF-SSD network model was modified from VGG16-SSD through replacing feature extractor by MobileNet and integrating multi-layer feature fusion structure. By optimizing and adjusting the network, three kinds of MF-SSD with different network structures (MF-SSD-cut-3, MF-SSD and MF-SSD-add-3) were tested, and the structure with the best detection performance was selected for corn silk detection. Based on the image data set of corn silk, two kinds of data augmentation techniques (randomly rotating original image from 0 to 180°, horizontally rolling over or translating original image) were applied to improve the training effect of the model. Whether using secondary training strategy and Focal loss to solve sample number imbalance was investigated, and the decrease process of Loss was compared and analyzed.

Result  The improved SSD model added with multi-layer feature fusion structure could improve the detection ability and recognition speed of network. Compared with VGG16-SSD, the average accuracy of intersection over union increased by 7.2%, the average recall of small target detection of corn silk increased by 19.6%, and the detection speed increased by 18.7%. In the embedded environment having high demand for storage space and run time, MF-SSD-cut-3 obtained shorter run time with smaller storage space in the premise of satisfying detection effect. MF-SSD obtained better detection effect in the condition of taking no account of storage space and run time. The secondary training strategy improved the network convergence speed and model stability. Focal loss effectively solved number imbalance problem of positive and negative samples, and made the training of network model more convergent.

Conclusion  The detection effect of the proposed MF-SSD model for small targets can meet the needs of real-time detection of corn silk in agricultural production. It can be used for automatic monitoring of corn growth state and yield prediction.