The Detection Algorithm For Small Targets In Aerial Images Based On Feature Fusion

In recent years,the target detection algorithm models emerge one after another,and remarkable progress has been made in target detection.Many applicable scenarios have been found in both the industrial field and the life field.However,most of the existing target detection models are designed for image targets in common natural scenes.For the small targets in aerial images,the detection effect is not very ideal,and there are great challenges.Therefore,this thesis starts from the actual scene of aerial images,and solves the problem of positioning and classification of small targets in aerial images from the perspectives of the fusion of shallow features and deep features,the enhancement of sensory field,and the lightweight feature extraction network.The main work is as follows:In order to realize the accurate detection of small target objects in aerial images,on the basis of SSD(Single Shot Multi Box Detector)detection model,an aerial image target detection model which is named MF-SSD based on multi-scale feature fusion and sensory field enhancement is proposed.This model constructs a feature fusion sub-network,it designs a two-level fusion strategy based on shallow features and a jumping connection fusion strategy based on middle features.The accuracy of target detection is low due to the small size of aerial target and less appearance information.MF-SSD combines with the feature graph information of multiple sizes,so the accuracy of small target detection is improved effectively.Meanwhile,on the basis of the feature fusion sub-network,a multibranch Inception structure which is named D-Inception based on dilated convolution is proposed as a sensor field enhancement module,which enhances the local information expression ability of the prediction feature layer and can better extract the target features and adapt to the target scale changes.In order to solve the problem of high computational complexity of the model caused by feature fusion and sensory field enhancement,a lightweight convolutional neural network is used to replace the basic network to extract the features of the target object,which can reduce the complexity of the network model while still maintaining high detection accuracy.The test was carried out on the aerial photography data set NWPU VHR-10,and the experimental results show that the improved target detection model MF-SSD has achieved better results than the original SSD model and Faster R-CNN model.It can detect the small target object in aerial photography efficiently and can be well generalized to the general target detection.