Detection of Rice Pests in Multi-species Unbalanced Samples Based on Improved YOLOv7-Tiny Algorithm
This study aimed to realize the detection of rice pests in field based on machine vision.Com-bined with the IP102 agricultural pest dataset and network resources,an unbalanced rice pest data set contai-ning 26 kinds of labels was established in this study.Through improving YOLOv7-tiny single-stage target de-tection algorithm,using partial convolution as the main convolution kernel,combining with the Polarized Self-Attention mechanism,and carrying out complex bidirectional multi-scale feature fusion for the extracted fea-tures,a rice pest detection model suitable for multi-species unbalanced samples was established.The results showed that under the conditions of adding transfer learning and multi-scale training,the average detection ac-curacy of the improved YOLOv7-tiny detection algorithm in the self-built rice pest data set was 96.4%,the de-tection time of single image was 8.8 ms,and the model size was 9 055 kb,so the rapid and accurate identifi-cation of rice pests in field could be realized.This study could provide technical supports for the intelligent de-tection and control of rice pests.
Rice pest detectionImproved YOLOv7-tiny algorithmPartial convolutionPolarized self-attentionFeature fusionTransfer learning
国家科技期刊平台
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