Abstract: In China, the current field survey methods of pest damage symptoms on rice canopy mainly rely on the forecasting technicians to estimate the sizes and numbers of damage symptoms by visual inspection for estimating the damage level of pests in paddy fields. The manual survey method is subjective, time-consuming, and labor-intensive. In this study, an improved RetinaNet model was proposed to automatically detect the damage symptom regions of two pests (Cnaphalocrocis medinalis and Chilo suppressalis) on rice canopy. This model was composed of one ResNeXt network, an improved feature pyramid network, and two fully convolutional networks (one was class subnet and the other was regression subnet). In this model, ResNeXt101 and Group Normalization were used as the feature extraction network and the normalization method respectively. The feature pyramid network was improved for achieving a higher detection rate of pest damage symptoms. The focal loss function was adopted in this model. All images were divided into two image sets including a training set and a testing set. The training images were augmented by flipping horizontally, enhancing contrast, and adding Gaussian noise methods to prevent overfitting problems. The damage symptom regions in training images were manually labeled by a labeling tool named LabelImg. 6 RetinaNet models based on VGG16, ResNet101, ResNeXt101, data augmentation, improved feature pyramid network, and different normalization methods respectively were developed and trained on the training set. These models were tested on the same testing set. Precision-Recall curves, average precisions and mean average precisions of six models were calculated to evaluate the detection effects of pest damage symptoms on 6 RetinaNet models. All models were trained and tested under the deep learning framework PyTorch and the operating system Ubuntu16.04. The Precision-Recall curves showed that the improved RetinaNet model could achieve higher precision in the same recall rates than the other 5 models. The mean average precision of the model based on ResNeXt101 was 12.37% higher than the model based on VGG16 and 0.95% higher than the model based on ResNet101. It meant that the ResNeXt101 could effectively extract the features of pest damage symptoms on rice canopy than VGG16 and ResNet101. The average precision of the model based on improved feature pyramid network increased by 4.93% in the detection of C. medinalis damage symptoms and the mean average precision increased by 3.36% in the detection of 2 pest damage symptoms. After data augmentation, the mean average precision of the improved model increased by 9.13%. It meant the data augmentation method could significantly improve the generalization ability of the model. The improved RetinaNet model based on ResNeXt101, improved feature pyramid network, group normalization and data augmentation achieved the average precision of 95.65% in the detection of C. medinalis damage symptoms and the average precision of 91.87% in the detection of C. suppressalis damage symptoms. The mean average precision of the damage symptom detection of 2 pests reached 93.76%. These results showed that the improved RetinaNet model improved the detection accuracy and robustness of pest damage symptoms on rice canopy. It took an average time of 0.56 s to detect one image using the improved RetinaNet model, which could meet the realtime detection task of pest damage symptoms on rice canopy. The improved RetinaNet model and its results would provide the field survey data and forecasting of damage symptoms of Cnaphalocrocis medinalis and Chilo suppressalis on the rice canopy. It could be applied in precision spraying pesticides and pest damage symptom patrol by unmanned aerial vehicles. It would realize the intelligent forecasting and monitoring of rice pests, reduce manpower expense, and improve the efficiency and accuracy of the field survey of pest damage symptoms on rice canopy.