Automatic Recognition of Agriculture Pests with Balanced Feature Pyramid Network

HIGHLIGHTSWe propose a balanced feature pyramid network model to achieve automatic recognition of agricultural pests.We propose and improved feature pyramid module to address the problems of the large semantic gap before feature fusion in FPN (feature pyramid network), information loss during feature fusion, and how to utilize complementary information to strengthen features after feature fusion.Our method is evaluated on the pest dataset and achieves better recognition results, the mAP (mean Average Precision) reaches 90.04%, and the final mAP after combining with the data enhancement strategy reaches 92.56%.Abstract.In order to ensure the steady improvement of the quality and efficiency of agricultural production, the precise prevention and control of agriculture pests should be given top priority. Based on the Faster R-CNN model, we propose a feature-augmented recognition model (FARM) for the detection of agricultural pests to improve the professional level of pest control. Firstly, we select VoVNet as the backbone network to extract rich visual information. Secondly, in view of the existing problems of FPN (Feature Pyramid Network), we propose a feature pyramid enhancement network to improve it. Finally, we use an adaptive anchor box network to train anchor boxes to avoid the problem of unbalanced positive and negative samples caused by artificial parameter adjustment. The experimental result shows that our method can achieve better recognition results; the mAP on the agricultural pest dataset reaches 90.04%, and the final mAP after combining with the data enhancement strategy reaches 92.56%. Keywords: Adaptive anchor box, Agricultural pests, Feature augmented, Feature pyramid.