Research On Satellite Cloud Image Detection Algorithm Based On Deep Learning

Through satellite cloud images,people can obtain different cloud information,among which convective clouds are easy to produce strong convective disastrous weather,affect aviation safety flight,and cause irreparable consequences to people's production and life when serious.At present,the method of artificial experience judgment is subjective and inefficient.Therefore,this paper proposes an improved SSD target detection algorithm that can efficiently and accurately detect convective clouds,which is of practical significance for weather prediction and aviation safety flight.This paper based on the SSD model with good detection speed and precision,first uses the VOC common data set to test the detection ability of the model,in view of its shortcomings,three improvement strategies are proposed.Then the improved SSD model is applied to the detection of convective clouds in satellite cloud images and named as cloud detection model.To solve the problem that the feature map used for prediction in hierarchical prediction of SSD model is not reused,this paper uses feature fusion method to improve the network structure,and realizes the fusion of conv4₃ and fc7 layers by feature connection and corresponding element addition.Then a new feature pyramid is generated on the fusion layer for multi-scale detection,so as to increase the relationship between the model feature layers,so that the feature of multiple layers is better utilized and the detection accuracy is also imroved.Aiming at the problem that the small target features in the front layer of the model are obvious but the semantic information is less,this paper adds feature enhancement module after the last improvement.Based on the idea of multi-branch convolution and residual network,A dilated convolution layer is introduced to expand the receptive field of features,which makes the feature information learned by the network richer,improves the detection accuracy and reduces the miss detection of small targets.Aiming at the problem that the online difficult sample mining mechanism in the SSD network eliminates the easy sample,which makes the easy sample unable to further improve the training effect,this paper removes the original online difficult sample mining mechanism,introduces the focal loss function into the SSD position regression loss function to balance the problem of positive and negative sample,enhances the model background resolution ability and reduces the miss detection rate.Because there is no public satellite cloud map data set,this paper selects the infrared cloud map of FY2 satellite,and the self-built data set is recorded as fy_cloud,Then the model is trained and tested after numbering,marking,grouping.The experimental results show that the accuracy of the cloud_detection model in the fy_cloud data set is 16.02%higher than that of the SSD model.According to the recognition results of satellite cloud images in each period,the overall detection results of convective cloud are good,the missing detection rate and false detection rate are low.