Oat Ears Detection and Counting Model in Natural Environment Based on Improved Faster R-CNN

In order to enable oat ears to be quickly and accurately identified in the natural environment, this paper proposes an oat ears detection and counting model based on an improved Faster R-CNN. In the backbone network, the commonly used single convolutional neural network is replaced by a parallel convolutional neural network to realize the feature extraction of oat ears, and a feature pyramid network (FPN) is incorporated to improve the small target-missed detection problem and the multi-scale problem of oat ears. Then, the anchor box configuration is optimized according to the size and distribution of the labeled boxes in the dataset, which improves the efficiency of the model to detect oat ears. Finally, progressive non-maximum suppression (Progressive-NMS) was used to replace non-maximum suppression (NMS) to optimize the screening process of prediction boxes. According to the data from different experiments designed, the optimized model can effectively detect oat ears in the natural environment and complete the counting of oat ears per unit area. Compared with the traditional Faster R-CNN detection model, the mean average precision (mAP) of the improved model is increased by 13.01%, which could provide reference for oat yield prediction and intelligent operation.